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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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• 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.

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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 – 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.

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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.

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An Overview of Qualitative Research Methods

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Qualitative research is a type of social science research that collects and works with non-numerical data and that seeks to interpret meaning from these data that help understand social life through the study of targeted populations or places.

People often frame it in opposition to quantitative research , which uses numerical data to identify large-scale trends and employs statistical operations to determine causal and correlative relationships between variables.

Within sociology, qualitative research is typically focused on the micro-level of social interaction that composes everyday life, whereas quantitative research typically focuses on macro-level trends and phenomena.

Key Takeaways

Methods of qualitative research include:

• observation and immersion
• open-ended surveys
• focus groups
• content analysis of visual and textual materials
• oral history

Qualitative research has a long history in sociology and has been used within it for as long as the field has existed.

This type of research has long appealed to social scientists because it allows the researchers to investigate the meanings people attribute to their behavior, actions, and interactions with others.

While quantitative research is useful for identifying relationships between variables, like, for example, the connection between poverty and racial hate, it is qualitative research that can illuminate why this connection exists by going directly to the source—the people themselves.

Qualitative research is designed to reveal the meaning that informs the action or outcomes that are typically measured by quantitative research. So qualitative researchers investigate meanings, interpretations, symbols, and the processes and relations of social life.

What this type of research produces is descriptive data that the researcher must then interpret using rigorous and systematic methods of transcribing, coding, and analysis of trends and themes.

Because its focus is everyday life and people's experiences, qualitative research lends itself well to creating new theories using the inductive method , which can then be tested with further research.

Qualitative researchers use their own eyes, ears, and intelligence to collect in-depth perceptions and descriptions of targeted populations, places, and events.

Their findings are collected through a variety of methods, and often a researcher will use at least two or several of the following while conducting a qualitative study:

• Direct observation : With direct observation, a researcher studies people as they go about their daily lives without participating or interfering. This type of research is often unknown to those under study, and as such, must be conducted in public settings where people do not have a reasonable expectation of privacy. For example, a researcher might observe the ways in which strangers interact in public as they gather to watch a street performer.
• Open-ended surveys : While many surveys are designed to generate quantitative data, many are also designed with open-ended questions that allow for the generation and analysis of qualitative data. For example, a survey might be used to investigate not just which political candidates voters chose, but why they chose them, in their own words.
• Focus group : In a focus group, a researcher engages a small group of participants in a conversation designed to generate data relevant to the research question. Focus groups can contain anywhere from 5 to 15 participants. Social scientists often use them in studies that examine an event or trend that occurs within a specific community. They are common in market research, too.
• In-depth interviews : Researchers conduct in-depth interviews by speaking with participants in a one-on-one setting. Sometimes a researcher approaches the interview with a predetermined list of questions or topics for discussion but allows the conversation to evolve based on how the participant responds. Other times, the researcher has identified certain topics of interest but does not have a formal guide for the conversation, but allows the participant to guide it.
• Oral history : The oral history method is used to create a historical account of an event, group, or community, and typically involves a series of in-depth interviews conducted with one or multiple participants over an extended period.
• Participant observation : This method is similar to observation, however with this one, the researcher also participates in the action or events to not only observe others but to gain the first-hand experience in the setting.
• Ethnographic observation : Ethnographic observation is the most intensive and in-depth observational method. Originating in anthropology, with this method, a researcher fully immerses themselves into the research setting and lives among the participants as one of them for anywhere from months to years. By doing this, the researcher attempts to experience day-to-day existence from the viewpoints of those studied to develop in-depth and long-term accounts of the community, events, or trends under observation.
• Content analysis : This method is used by sociologists to analyze social life by interpreting words and images from documents, film, art, music, and other cultural products and media. The researchers look at how the words and images are used, and the context in which they are used to draw inferences about the underlying culture. Content analysis of digital material, especially that generated by social media users, has become a popular technique within the social sciences.

While much of the data generated by qualitative research is coded and analyzed using just the researcher's eyes and brain, the use of computer software to do these processes is increasingly popular within the social sciences.

Such software analysis works well when the data is too large for humans to handle, though the lack of a human interpreter is a common criticism of the use of computer software.

Pros and Cons

Qualitative research has both benefits and drawbacks.

On the plus side, it creates an in-depth understanding of the attitudes, behaviors, interactions, events, and social processes that comprise everyday life. In doing so, it helps social scientists understand how everyday life is influenced by society-wide things like social structure , social order , and all kinds of social forces.

This set of methods also has the benefit of being flexible and easily adaptable to changes in the research environment and can be conducted with minimal cost in many cases.

Among the downsides of qualitative research is that its scope is fairly limited so its findings are not always widely able to be generalized.

Researchers also have to use caution with these methods to ensure that they do not influence the data in ways that significantly change it and that they do not bring undue personal bias to their interpretation of the findings.

Fortunately, qualitative researchers receive rigorous training designed to eliminate or reduce these types of research bias.

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• Pros and Cons of Secondary Data Analysis
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• The Different Types of Sampling Designs in Sociology
• Principal Components and Factor Analysis
• Sociology Explains Why Some People Cheat on Their Spouses
• Deductive Versus Inductive Reasoning
• Data Sources For Sociological Research
• How to Construct an Index for Research
• A Review of Software Tools for Quantitative Data Analysis
• Constructing a Deductive Theory
• Scales Used in Social Science Research

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9.1 Qualitative research: What is it and when should it be used?

Learning objectives.

• Define qualitative research
• Explain the differences between qualitative and quantitative research
• Identify the benefits and challenges of qualitative research

Qualitative versus quantitative research methods refers to data-oriented considerations about the type of data to collected and how they are analyzed. Qualitative research relies mostly on non-numeric data, such as interviews and observations to understand their meaning, in contrast to quantitative research which employs numeric data such as scores and metrics. Hence, qualitative research is not amenable to statistical procedures, but is coded using techniques like content analysis. Sometimes, coded qualitative data are tabulated quantitatively as frequencies of codes, but this data is not statistically analyzed.  Qualitative research has its roots in anthropology, sociology, psychology, linguistics, and semiotics, and has been available since the early 19th century, long before quantitative statistical techniques were employed.

Distinctions from Quantitative Research

In qualitative research, the role of the researcher receives critical attention.  In some methods such as ethnography, action research, and participant observation, the researcher is considered part of the social phenomenon, and her specific role and involvement in the research process must be made clear during data analysis. In other methods, such as case research, the researcher must take a “neutral” or unbiased stance during the data collection and analysis processes, and ensure that her personal biases or preconceptions does not taint the nature of subjective inferences derived from qualitative research.

Analysis in qualitative research is holistic and contextual, rather than being reductionist and isolationist. Qualitative interpretations tend to focus on language, signs, and meanings from the perspective of the participants involved in the social phenomenon, in contrast to statistical techniques that are employed heavily in positivist research. Rigor in qualitative research is viewed in terms of systematic and transparent approaches for data collection and analysis rather than statistical benchmarks for construct validity or significance testing.

Lastly, data collection and analysis can proceed simultaneously and iteratively in qualitative research. For instance, the researcher may conduct an interview and code it before proceeding to the next interview. Simultaneous analysis helps the researcher correct potential flaws in the interview protocol or adjust it to capture the phenomenon of interest better. The researcher may even change her original research question if she realizes that her original research questions are unlikely to generate new or useful insights. This is a valuable but often understated benefit of qualitative research, and is not available in quantitative research, where the research project cannot be modified or changed once the data collection has started without redoing the entire project from the start.

Benefits and Challenges of Qualitative Research

Qualitative research has several unique advantages. First, it is well-suited for exploring hidden reasons behind complex, interrelated, or multifaceted social processes, such as inter-firm relationships or inter-office politics, where quantitative evidence may be biased, inaccurate, or otherwise difficult to obtain. Second, it is often helpful for theory construction in areas with no or insufficient pre-existing theory. Third, qualitative research is also appropriate for studying context-specific, unique, or idiosyncratic events or processes. Fourth, it can help uncover interesting and relevant research questions and issues for follow-up research.

At the same time, qualitative research also has its own set of challenges. First, this type of research tends to be more time and resource intensive than quantitative research in data collection and analytic efforts. Too little data can lead to false or premature assumptions, while too much data may not be effectively processed by the researcher. Second, qualitative research requires well-trained researchers who are capable of seeing and interpreting complex social phenomenon from the perspectives of the embedded participants and reconciling the diverse perspectives of these participants, without injecting their personal biases or preconceptions into their inferences. Third, all participants or data sources may not be equally credible, unbiased, or knowledgeable about the phenomenon of interest, or may have undisclosed political agendas, which may lead to misleading or false impressions. Inadequate trust between participants and researcher may hinder full and honest self-representation by participants, and such trust building takes time. It is the job of the qualitative researcher to “see through the smoke” (hidden or biased agendas) and understand the true nature of the problem. Finally, given the heavily contextualized nature of inferences drawn from qualitative research, such inferences do not lend themselves well to replicability or generalizability.

Key Takeaways

• Qualitative research examines words and other non-numeric media
• Analysis in qualitative research is holistic and contextual
• Qualitative research offers unique benefits, while facing challenges to generalizability and replicability
• Qualitative methods – examine words or other media to understand their meaning

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative Research Definition

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Table of Contents

Qualitative research seeks to understand people’s experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people’s beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or service, such as in user experience (UX) design or marketing . 

Researchers use qualitative approaches to “determine answers to research questions on human behavior and the cultural values that drive our thinking and behavior,” says Margaret J. King, director at The Center for Cultural Studies & Analysis in Philadelphia.

Data in qualitative research typically can’t be assessed mathematically — the data is not sets of numbers or quantifiable information. Rather, it’s collections of images, words, notes on behaviors, descriptions of emotions, and historical context. Data is collected through observations, interviews, surveys, focus groups, and secondary research. 

However, a qualitative study needs a “clear research question at its base,” notes King, and the research needs to be “observed, categorized, compared, and evaluated (along a scale or by a typology chart) by reference to a baseline in order to determine an outcome with value as new and reliable information.”

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Who Uses Qualitative Research?

Researchers in social sciences and humanities often use qualitative research methods, especially in specific areas of study like anthropology, history, education, and sociology. 

Qualitative methods are also applicable in business, technology , and marketing spaces. For example, product managers use qualitative research to understand how target audiences respond to their products. They may use focus groups to gain insights from potential customers on product prototypes and improvements or surveys from existing customers to understand what changes users want to see. 

Other careers that may involve qualitative research include: 

• Marketing analyst
• UX and UI analyst
• Market researcher
• Statistician
• Business analyst
• Data analyst
• Research assistant
• Claims investigator

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Good research begins with a question, and this question informs the approach used by qualitative researchers. 

Grounded Theory

Grounded theory is an inductive approach to theory development. In many forms of research, you begin with a hypothesis and then test it to see if you’re correct. In grounded theory, though, you go in without any assumptions and rely on the data you collect to form theories. You start with an open question about a phenomenon you are studying and collect and analyze data until you can form a fully-fledged theory from the information. 

Example: A company wants to improve its brand and marketing strategies. The company performs a grounded theory approach to solving this problem by conducting interviews and surveys with past, current, and prospective customers. The information gathered from these methods helps the company understand what type of branding and marketing their customer-base likes and dislikes, allowing the team to inductively craft a new brand and marketing strategy from the data. 

Action Research

Action research is one part study and one part problem-solving . Through action research, analysts investigate a problem or weakness and develop practical solutions. The process of action research is cyclical —- researchers assess solutions for efficiency and effectiveness, and create further solutions to correct any issues found. 

Example: A manager notices her employees struggle to cooperate on group projects. She carefully reviews how team members interact with each other and asks them all to respond to a survey about communication. Through the survey and study, she finds that guidelines for group projects are unclear. After changing the guidelines, she reviews her team again to see if there are any changes to their behavior.  

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Phenomenological Research

Phenomenological research investigates a phenomenon in depth, looking at people’s experiences and understanding of the situation. This sort of study is primarily descriptive and seeks to broaden understanding around a specific incident and the people involved. Researchers in phenomenological studies must be careful to set aside any biases or assumptions because the information used should be entirely from the subjects themselves. 

Example : A researcher wants to better understand the lived experience of college students with jobs. The purpose of this research is to gain insights into the pressures of college students who balance studying and working at the same time. The researcher conducts a series of interviews with several college students, learning about their past and current situations. Through the first few interviews, the researcher builds a relationship with the students. Later discussions are more targeted, with questions prompting the students to discuss their emotions surrounding both work and school and the difficulties and benefits arising from their situation. The researcher then analyzes these interviews, and identifies shared themes to contextualize the experiences of the students.

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Ethnography

Ethnography is an immersive study of a particular culture or community. Through ethnographic research, analysts aim to learn about a group’s conventions, social dynamics, and cultural norms. Some researchers use active observation methods, finding ways to integrate themselves into the culture as much as possible. Others use passive observation, watching closely from the outside but not fully immersing themselves. 

Example: A company hires an external researcher to learn what their company’s culture is actually like. The researcher studies the social dynamics of the employees and may even look at how these employees interact with clients and with each other outside of the office. The goal is to deliver a comprehensive report of the company’s culture and the social dynamics of its employees.

Case Studies

A case study is a type of in-depth analysis of a situation. Case studies can focus on an organization, belief system, event, person, or action. The goal of a case study is to understand the phenomenon and put it in a real-world context. Case studies are also commonly used in marketing and sales to highlight the benefits of a company’s products or services. 

Example: A business performs a case study of its competitors’ strategies. This case study aims to show why the company should adopt a specific business strategy. The study looks at each competitor’s business structure, marketing campaigns, product offerings, and historical growth trends. Then, using this data on other businesses, the researcher can theorize how that strategy would benefit their company.

>>MORE: Learn how companies use case study interviews to assess candidates’ research and problem-solving skills. 

Qualitative research methods are great for generating new ideas. The exploratory nature of qualitative research means uncovering unexpected information, which often leads to new theories and further research topics. Additionally, qualitative findings feel meaningful. These studies focus on people, emotions, and societies and may feel closer to their communities than quantitative research that relies on more mathematical and logical data. 

However, qualitative research can be unreliable at times. It’s difficult to replicate qualitative studies since people’s opinions and emotions can change quickly. For example, a focus group has a lot of variables that can affect the outcome, and that same group, asked the same questions a year later, may have entirely different responses. The data collection can also be difficult and time-consuming with qualitative research. Ultimately, interviewing people, reviewing surveys, and understanding and explaining human emotions can be incredibly complex.

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While qualitative research deals with data that isn’t easily manipulated by mathematics, quantitative research almost exclusively involves numbers and numerical data. Quantitative studies aim to find concrete details, like units of time, percentages, or statistics. 

Besides the types of data used, a core difference between quantitative and qualitative research is the idea of control and replication. 

“Qualitative is less subject to control (as in lab studies) and, therefore, less statistically measurable than quantitative approaches,” says King.

One person’s interview about a specific topic can have completely different responses than every other person’s interview since there are so many variables in qualitative research. On the other hand, quantitative studies can often be replicated. For instance, when testing the effects of a new medication, quantifiable data, like blood test results, can be repeated. Qualitative data, though, like how people feel about the medication, may differ from person to person and from moment to moment.

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You can show your experience with qualitative research on your resume in your skills or work experience sections and your cover letter . 

• In your skills section , you can list types of qualitative research you are skilled at, like conducting interviews, performing grounded theory research, or crafting case studies. 
• In your work or internship experience descriptions , you can highlight specific examples, like talking about a time you used action research to solve a complex issue at your last job. 
• In your cover letter , you can discuss in-depth qualitative research projects you’ve completed. For instance, say you spent a summer conducting ethnographic research or a whole semester running focus groups to get feedback on a product. You can talk about these experiences in your cover letter and note how these skills make you a great fit for the job. 

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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.

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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 vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, 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. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Choosing a Qualitative Research Approach

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Editor's Note: The online version of this article contains a list of further reading resources and the authors' professional information .

The Challenge

Educators often pose questions about qualitative research. For example, a program director might say: “I collect data from my residents about their learning experiences in a new longitudinal clinical rotation. If I want to know about their learning experiences, should I use qualitative methods? I have been told that there are many approaches from which to choose. Someone suggested that I use grounded theory, but how do I know this is the best approach? Are there others?”

What Is Known

Qualitative research is the systematic inquiry into social phenomena in natural settings. These phenomena can include, but are not limited to, how people experience aspects of their lives, how individuals and/or groups behave, how organizations function, and how interactions shape relationships. In qualitative research, the researcher is the main data collection instrument. The researcher examines why events occur, what happens, and what those events mean to the participants studied. 1 , 2

Qualitative research starts from a fundamentally different set of beliefs—or paradigms—than those that underpin quantitative research. Quantitative research is based on positivist beliefs that there is a singular reality that can be discovered with the appropriate experimental methods. Post-positivist researchers agree with the positivist paradigm, but believe that environmental and individual differences, such as the learning culture or the learners' capacity to learn, influence this reality, and that these differences are important. Constructivist researchers believe that there is no single reality, but that the researcher elicits participants' views of reality. 3 Qualitative research generally draws on post-positivist or constructivist beliefs.

Qualitative scholars develop their work from these beliefs—usually post-positivist or constructivist—using different approaches to conduct their research. In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question(s), the method(s) of data collection, and how data are analyzed. 4 , 5

Choosing a Qualitative Approach

Before engaging in any qualitative study, consider how your views about what is possible to study will affect your approach. Then select an appropriate approach within which to work. Alignment between the belief system underpinning the research approach, the research question, and the research approach itself is a prerequisite for rigorous qualitative research. To enhance the understanding of how different approaches frame qualitative research, we use this introductory challenge as an illustrative example.

The clinic rotation in a program director's training program was recently redesigned as a longitudinal clinical experience. Resident satisfaction with this rotation improved significantly following implementation of the new longitudinal experience. The program director wants to understand how the changes made in the clinic rotation translated into changes in learning experiences for the residents.

Qualitative research can support this program director's efforts. Qualitative research focuses on the events that transpire and on outcomes of those events from the perspectives of those involved. In this case, the program director can use qualitative research to understand the impact of the new clinic rotation on the learning experiences of residents. The next step is to decide which approach to use as a frame for the study.

The table lists the purpose of 3 commonly used approaches to frame qualitative research. For each frame, we provide an example of a research question that could direct the study and delineate what outcomes might be gained by using that particular approach.

Methodology Overview

How You Can Start TODAY

• 1 Examine the foundations of the existing literature: As part of the literature review, make note of what is known about the topic and which approaches have been used in prior studies. A decision should be made to determine the extent to which the new study is exploratory and the extent to which findings will advance what is already known about the topic.
• 2 Find a qualitatively skilled collaborator: If you are interested in doing qualitative research, you should consult with a qualitative expert. Be prepared to talk to the qualitative scholar about what you would like to study and why . Furthermore, be ready to describe the literature to date on the topic (remember, you are asking for this person's expertise regarding qualitative approaches—he or she won't necessarily have content expertise). Qualitative research must be designed and conducted with rigor (rigor will be discussed in Rip Out No. 8 of this series). Input from a qualitative expert will ensure that rigor is employed from the study's inception.
• 3 Consider the approach: With a literature review completed and a qualitatively skilled collaborator secured, it is time to decide which approach would be best suited to answering the research question. Questions to consider when weighing approaches might include the following:
• • Will my findings contribute to the creation of a theoretical model to better understand the area of study? ( grounded theory )
• • Will I need to spend an extended amount of time trying to understand the culture and process of a particular group of learners in their natural context? ( ethnography )
• • Is there a particular phenomenon I want to better understand/describe? ( phenomenology )

What You Can Do LONG TERM

• 1 Develop your qualitative research knowledge and skills : A basic qualitative research textbook is a valuable investment to learn about qualitative research (further reading is provided as online supplemental material). A novice qualitative researcher will also benefit from participating in a massive online open course or a mini-course (often offered by professional organizations or conferences) that provides an introduction to qualitative research. Most of all, collaborating with a qualitative researcher can provide the support necessary to design, execute, and report on the study.
• 2 Undertake a pilot study: After learning about qualitative methodology, the next best way to gain expertise in qualitative research is to try it in a small scale pilot study with the support of a qualitative expert. Such application provides an appreciation for the thought processes that go into designing a study, analyzing the data, and reporting on the findings. Alternatively, if you have the opportunity to work on a study led by a qualitative expert, take it! The experience will provide invaluable opportunities for learning how to engage in qualitative research.

Supplementary Material

The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Uniformed Services University of the Health Sciences, the Department of the Navy, the Department of Defense, or the US government.

References and Resources for Further Reading

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Part 2: Conceptualizing your research project

9. Writing your research question

Chapter outline.

• Empirical vs. ethical questions (4 minute read)
• Characteristics of a good research question (4 minute read)
• Quantitative research questions (7 minute read)
• Qualitative research questions (3 minute read)
• Evaluating and updating your research questions (4 minute read)

Content warning: examples in this chapter include references to sexual violence, sexism, substance use disorders, homelessness, domestic violence, the child welfare system, cissexism and heterosexism, and truancy and school discipline.

9.1 Empirical vs. ethical questions

Learning objectives.

Learners will be able to…

• Define empirical questions and provide an example
• Define ethical questions and provide an example

Writing a good research question is an art and a science. It is a science because you have to make sure it is clear, concise, and well-developed. It is an art because often your language needs “wordsmithing” to perfect and clarify the meaning. This is an exciting part of the research process; however, it can also be one of the most stressful.

Creating a good research question begins by identifying a topic you are interested in studying. At this point, you already have a working question. You’ve been applying it to the exercises in each chapter, and after reading more about your topic in the scholarly literature, you’ve probably gone back and revised your working question a few times. We’re going to continue that process in more detail in this chapter. Keep in mind that writing research questions is an iterative process, with revisions happening week after week until you are ready to start your project.

Empirical vs. ethical questions

When it comes to research questions, social science is best equipped to answer empirical questions —those that can be answered by real experience in the real world—as opposed to  ethical questions —questions about which people have moral opinions and that may not be answerable in reference to the real world. While social workers have explicit ethical obligations (e.g., service, social justice), research projects ask empirical questions to help actualize and support the work of upholding those ethical principles.

In order to help you better understand the difference between ethical and empirical questions, let’s consider a topic about which people have moral opinions. How about SpongeBob SquarePants? [1] In early 2005, members of the conservative Christian group Focus on the Family (2005) [2] denounced this seemingly innocuous cartoon character as “morally offensive” because they perceived his character to be one that promotes a “pro-gay agenda.” Focus on the Family supported their claim that SpongeBob is immoral by citing his appearance in a children’s video designed to promote tolerance of all family forms (BBC News, 2005). [3] They also cited SpongeBob’s regular hand-holding with his male sidekick Patrick as further evidence of his immorality.

So, can we now conclude that SpongeBob SquarePants is immoral? Not so fast. While your mother or a newspaper or television reporter may provide an answer, a social science researcher cannot. Questions of morality are ethical, not empirical. Of course, this doesn’t mean that social science researchers cannot study opinions about or social meanings surrounding SpongeBob SquarePants (Carter, 2010). [4] We study humans after all, and as you will discover in the following chapters of this textbook, we are trained to utilize a variety of scientific data-collection techniques to understand patterns of human beliefs and behaviors. Using these techniques, we could find out how many people in the United States find SpongeBob morally reprehensible, but we could never learn, empirically, whether SpongeBob is in fact morally reprehensible.

Let’s consider an example from a recent MSW research class I taught. A student group wanted to research the penalties for sexual assault. Their original research question was: “How can prison sentences for sexual assault be so much lower than the penalty for drug possession?” Outside of the research context, that is a darn good question! It speaks to how the War on Drugs and the patriarchy have distorted the criminal justice system towards policing of drug crimes over gender-based violence.

Unfortunately, it is an ethical question, not an empirical one. To answer that question, you would have to draw on philosophy and morality, answering what it is about human nature and society that allows such unjust outcomes. However, you could not answer that question by gathering data about people in the real world. If I asked people that question, they would likely give me their opinions about drugs, gender-based violence, and the criminal justice system. But I wouldn’t get the real answer about why our society tolerates such an imbalance in punishment.

As the students worked on the project through the semester, they continued to focus on the topic of sexual assault in the criminal justice system. Their research question became more empirical because they read more empirical articles about their topic. One option that they considered was to evaluate intervention programs for perpetrators of sexual assault to see if they reduced the likelihood of committing sexual assault again. Another option they considered was seeing if counties or states with higher than average jail sentences for sexual assault perpetrators had lower rates of re-offense for sexual assault. These projects addressed the ethical question of punishing perpetrators of sexual violence but did so in a way that gathered and analyzed empirical real-world data. Our job as social work researchers is to gather social facts about social work issues, not to judge or determine morality.

Key Takeaways

• Empirical questions are distinct from ethical questions.
• There are usually a number of ethical questions and a number of empirical questions that could be asked about any single topic.
• While social workers may research topics about which people have moral opinions, a researcher’s job is to gather and analyze empirical data.
• Take a look at your working question. Make sure you have an empirical question, not an ethical one. To perform this check, describe how you could find an answer to your question by conducting a study, like a survey or focus group, with real people.

9.2 Characteristics of a good research question

• Identify and explain the key features of a good research question
• Explain why it is important for social workers to be focused and clear with the language they use in their research questions

Now that you’ve made sure your working question is empirical, you need to revise that working question into a formal research question. So, what makes a good research question? First, it is generally written in the form of a question. To say that your research question is “the opioid epidemic” or “animal assisted therapy” or “oppression” would not be correct. You need to frame your topic as a question, not a statement. A good research question is also one that is well-focused. A well-focused question helps you tune out irrelevant information and not try to answer everything about the world all at once. You could be the most eloquent writer in your class, or even in the world, but if the research question about which you are writing is unclear, your work will ultimately lack direction.

In addition to being written in the form of a question and being well-focused, a good research question is one that cannot be answered with a simple yes or no. For example, if your interest is in gender norms, you could ask, “Does gender affect a person’s performance of household tasks?” but you will have nothing left to say once you discover your yes or no answer. Instead, why not ask, about the relationship between gender and household tasks. Alternatively, maybe we are interested in how or to what extent gender affects a person’s contributions to housework in a marriage? By tweaking your question in this small way, you suddenly have a much more fascinating question and more to say as you attempt to answer it.

A good research question should also have more than one plausible answer. In the example above, the student who studied the relationship between gender and household tasks had a specific interest in the impact of gender, but she also knew that preferences might be impacted by other factors. For example, she knew from her own experience that her more traditional and socially conservative friends were more likely to see household tasks as part of the female domain, and were less likely to expect their male partners to contribute to those tasks. Thinking through the possible relationships between gender, culture, and household tasks led that student to realize that there were many plausible answers to her questions about how  gender affects a person’s contribution to household tasks. Because gender doesn’t exist in a vacuum, she wisely felt that she needed to consider other characteristics that work together with gender to shape people’s behaviors, likes, and dislikes. By doing this, the student considered the third feature of a good research question–she thought about relationships between several concepts. While she began with an interest in a single concept—household tasks—by asking herself what other concepts (such as gender or political orientation) might be related to her original interest, she was able to form a question that considered the relationships  among  those concepts.

This student had one final component to consider. Social work research questions must contain a target population. Her study would be very different if she were to conduct it on older adults or immigrants who just arrived in a new country. The target population is the group of people whose needs your study addresses. Maybe the student noticed issues with household tasks as part of her social work practice with first-generation immigrants, and so she made it her target population. Maybe she wants to address the needs of another community. Whatever the case, the target population should be chosen while keeping in mind social work’s responsibility to work on behalf of marginalized and oppressed groups.

In sum, a good research question generally has the following features:

• It is written in the form of a question
• It is clearly written
• It cannot be answered with “yes” or “no”
• It has more than one plausible answer
• It considers relationships among multiple variables
• It is specific and clear about the concepts it addresses
• It includes a target population
• A poorly focused research question can lead to the demise of an otherwise well-executed study.
• Research questions should be clearly worded, consider relationships between multiple variables, have more than one plausible answer, and address the needs of a target population.

Okay, it’s time to write out your first draft of a research question.

• Once you’ve done so, take a look at the checklist in this chapter and see if your research question meets the criteria to be a good one.

Brainstorm whether your research question might be better suited to quantitative or qualitative methods.

• Describe why your question fits better with quantitative or qualitative methods.
• Provide an alternative research question that fits with the other type of research method.

9.3 Quantitative research questions

• Describe how research questions for exploratory, descriptive, and explanatory quantitative questions differ and how to phrase them
• Identify the differences between and provide examples of strong and weak explanatory research questions

Quantitative descriptive questions

The type of research you are conducting will impact the research question that you ask. Probably the easiest questions to think of are quantitative descriptive questions. For example, “What is the average student debt load of MSW students?” is a descriptive question—and an important one. We aren’t trying to build a causal relationship here. We’re simply trying to describe how much debt MSW students carry. Quantitative descriptive questions like this one are helpful in social work practice as part of community scans, in which human service agencies survey the various needs of the community they serve. If the scan reveals that the community requires more services related to housing, child care, or day treatment for people with disabilities, a nonprofit office can use the community scan to create new programs that meet a defined community need.

Quantitative descriptive questions will often ask for percentage, count the number of instances of a phenomenon, or determine an average. Descriptive questions may only include one variable, such as ours about student debt load, or they may include multiple variables. Because these are descriptive questions, our purpose is not to investigate causal relationships between variables. To do that, we need to use a quantitative explanatory question.

Quantitative explanatory questions

Most studies you read in the academic literature will be quantitative and explanatory. Why is that? If you recall from Chapter 2 , explanatory research tries to build nomothetic causal relationships. They are generalizable across space and time, so they are applicable to a wide audience. The editorial board of a journal wants to make sure their content will be useful to as many people as possible, so it’s not surprising that quantitative research dominates the academic literature.

Structurally, quantitative explanatory questions must contain an independent variable and dependent variable. Questions should ask about the relationship between these variables. The standard format I was taught in graduate school for an explanatory quantitative research question is: “What is the relationship between [independent variable] and [dependent variable] for [target population]?” You should play with the wording for your research question, revising that standard format to match what you really want to know about your topic.

Let’s take a look at a few more examples of possible research questions and consider the relative strengths and weaknesses of each. Table 9.1 does just that. While reading the table, keep in mind that I have only noted what I view to be the most relevant strengths and weaknesses of each question. Certainly each question may have additional strengths and weaknesses not noted in the table. Each of these questions is drawn from student projects in my research methods classes and reflects the work of many students on their research question over many weeks.

Making it more specific

A good research question should also be specific and clear about the concepts it addresses. A student investigating gender and household tasks knows what they mean by “household tasks.” You likely also have an impression of what “household tasks” means. But are your definition and the student’s definition the same? A participant in their study may think that managing finances and performing home maintenance are household tasks, but the researcher may be interested in other tasks like childcare or cleaning. The only way to ensure your study stays focused and clear is to be specific about what you mean by a concept. The student in our example could pick a specific household task that was interesting to them or that the literature indicated was important—for example, childcare. Or, the student could have a broader view of household tasks, one that encompasses childcare, food preparation, financial management, home repair, and care for relatives. Any option is probably okay, as long as the researcher is clear on what they mean by “household tasks.” Clarifying these distinctions is important as we look ahead to specifying how your variables will be measured in Chapter 11 .

Table 9.2 contains some “watch words” that indicate you may need to be more specific about the concepts in your research question.

It can be challenging to be this specific in social work research, particularly when you are just starting out your project and still reading the literature. If you’ve only read one or two articles on your topic, it can be hard to know what you are interested in studying. Broad questions like “What are the causes of chronic homelessness, and what can be done to prevent it?” are common at the beginning stages of a research project as working questions. However, moving from working questions to research questions in your research proposal requires that you examine the literature on the topic and refine your question over time to be more specific and clear. Perhaps you want to study the effect of a specific anti-homelessness program that you found in the literature. Maybe there is a particular model to fighting homelessness, like Housing First or transitional housing, that you want to investigate further. You may want to focus on a potential cause of homelessness such as LGBTQ+ discrimination that you find interesting or relevant to your practice. As you can see, the possibilities for making your question more specific are almost infinite.

Quantitative exploratory questions

In exploratory research, the researcher doesn’t quite know the lay of the land yet. If someone is proposing to conduct an exploratory quantitative project, the watch words highlighted in Table 9.2 are not problematic at all. In fact, questions such as “What factors influence the removal of children in child welfare cases?” are good because they will explore a variety of factors or causes. In this question, the independent variable is less clearly written, but the dependent variable, family preservation outcomes, is quite clearly written. The inverse can also be true. If we were to ask, “What outcomes are associated with family preservation services in child welfare?”, we would have a clear independent variable, family preservation services, but an unclear dependent variable, outcomes. Because we are only conducting exploratory research on a topic, we may not have an idea of what concepts may comprise our “outcomes” or “factors.” Only after interacting with our participants will we be able to understand which concepts are important.

Remember that exploratory research is appropriate only when the researcher does not know much about topic because there is very little scholarly research. In our examples above, there is extensive literature on the outcomes in family reunification programs and risk factors for child removal in child welfare. Make sure you’ve done a thorough literature review to ensure there is little relevant research to guide you towards a more explanatory question.

• Descriptive quantitative research questions are helpful for community scans but cannot investigate causal relationships between variables.
• Explanatory quantitative research questions must include an independent and dependent variable.
• Exploratory quantitative research questions should only be considered when there is very little previous research on your topic.
• Identify the type of research you are engaged in (descriptive, explanatory, or exploratory).
• Create a quantitative research question for your project that matches with the type of research you are engaged in.

Preferably, you should be creating an explanatory research question for quantitative research.

9.4 Qualitative research questions

• List the key terms associated with qualitative research questions
• Distinguish between qualitative and quantitative research questions

Qualitative research questions differ from quantitative research questions. Because qualitative research questions seek to explore or describe phenomena, not provide a neat nomothetic explanation, they are often more general and openly worded. They may include only one concept, though many include more than one. Instead of asking how one variable causes changes in another, we are instead trying to understand the experiences ,  understandings , and  meanings that people have about the concepts in our research question. These keywords often make an appearance in qualitative research questions.

Let’s work through an example from our last section. In Table 9.1, a student asked, “What is the relationship between sexual orientation or gender identity and homelessness for late adolescents in foster care?” In this question, it is pretty clear that the student believes that adolescents in foster care who identify as LGBTQ+ may be at greater risk for homelessness. This is a nomothetic causal relationship—LGBTQ+ status causes changes in homelessness.

However, what if the student were less interested in  predicting  homelessness based on LGBTQ+ status and more interested in  understanding  the stories of foster care youth who identify as LGBTQ+ and may be at risk for homelessness? In that case, the researcher would be building an idiographic causal explanation . The youths whom the researcher interviews may share stories of how their foster families, caseworkers, and others treated them. They may share stories about how they thought of their own sexuality or gender identity and how it changed over time. They may have different ideas about what it means to transition out of foster care.

Because qualitative questions usually center on idiographic causal relationships, they look different than quantitative questions. Table 9.3 below takes the final research questions from Table 9.1 and adapts them for qualitative research. The guidelines for research questions previously described in this chapter still apply, but there are some new elements to qualitative research questions that are not present in quantitative questions.

• Qualitative research questions often ask about lived experience, personal experience, understanding, meaning, and stories.
• Qualitative research questions may be more general and less specific.
• Qualitative research questions may also contain only one variable, rather than asking about relationships between multiple variables.

Qualitative research questions have one final feature that distinguishes them from quantitative research questions: they can change over the course of a study. Qualitative research is a reflexive process, one in which the researcher adapts their approach based on what participants say and do. The researcher must constantly evaluate whether their question is important and relevant to the participants. As the researcher gains information from participants, it is normal for the focus of the inquiry to shift.

For example, a qualitative researcher may want to study how a new truancy rule impacts youth at risk of expulsion. However, after interviewing some of the youth in their community, a researcher might find that the rule is actually irrelevant to their behavior and thoughts. Instead, their participants will direct the discussion to their frustration with the school administrators or the lack of job opportunities in the area. This is a natural part of qualitative research, and it is normal for research questions and hypothesis to evolve based on information gleaned from participants.

However, this reflexivity and openness unacceptable in quantitative research for good reasons. Researchers using quantitative methods are testing a hypothesis, and if they could revise that hypothesis to match what they found, they could never be wrong! Indeed, an important component of open science and reproducability is the preregistration of a researcher’s hypotheses and data analysis plan in a central repository that can be verified and replicated by reviewers and other researchers. This interactive graphic from 538 shows how an unscrupulous research could come up with a hypothesis and theoretical explanation  after collecting data by hunting for a combination of factors that results in a statistically significant relationship. This is an excellent example of how the positivist assumptions behind quantitative research and intepretivist assumptions behind qualitative research result in different approaches to social science.

• Qualitative research questions often contain words or phrases like “lived experience,” “personal experience,” “understanding,” “meaning,” and “stories.”
• Qualitative research questions can change and evolve over the course of the study.
• Using the guidance in this chapter, write a qualitative research question. You may want to use some of the keywords mentioned above.

9.5 Evaluating and updating your research questions

• Evaluate the feasibility and importance of your research questions
• Begin to match your research questions to specific designs that determine what the participants in your study will do

Feasibility and importance

As you are getting ready to finalize your research question and move into designing your research study, it is important to check whether your research question is feasible for you to answer and what importance your results will have in the community, among your participants, and in the scientific literature

Key questions to consider when evaluating your question’s feasibility include:

• Do you have access to the data you need?
• Will you be able to get consent from stakeholders, gatekeepers, and others?
• Does your project pose risk to individuals through direct harm, dual relationships, or breaches in confidentiality? (see Chapter 6 for more ethical considerations)
• Are you competent enough to complete the study?
• Do you have the resources and time needed to carry out the project?

Key questions to consider when evaluating the importance of your question include:

• Can we answer your research question simply by looking at the literature on your topic?
• How does your question add something new to the scholarly literature? (raises a new issue, addresses a controversy, studies a new population, etc.)
• How will your target population benefit, once you answer your research question?
• How will the community, social work practice, and the broader social world benefit, once you answer your research question?
• Using the questions above, check whether you think your project is feasible for you to complete, given the constrains that student projects face.
• Realistically, explore the potential impact of your project on the community and in the scientific literature. Make sure your question cannot be answered by simply reading more about your topic.

Matching your research question and study design

This chapter described how to create a good quantitative and qualitative research question. In Parts 3 and 4 of this textbook, we will detail some of the basic designs like surveys and interviews that social scientists use to answer their research questions. But which design should you choose?

As with most things, it all depends on your research question. If your research question involves, for example, testing a new intervention, you will likely want to use an experimental design. On the other hand, if you want to know the lived experience of people in a public housing building, you probably want to use an interview or focus group design.

We will learn more about each one of these designs in the remainder of this textbook. We will also learn about using data that already exists, studying an individual client inside clinical practice, and evaluating programs, which are other examples of designs. Below is a list of designs we will cover in this textbook:

• Surveys: online, phone, mail, in-person
• Experiments: classic, pre-experiments, quasi-experiments
• Interviews: in-person or via phone or videoconference
• Focus groups: in-person or via videoconference
• Content analysis of existing data
• Secondary data analysis of another researcher’s data
• Program evaluation

The design of your research study determines what you and your participants will do. In an experiment, for example, the researcher will introduce a stimulus or treatment to participants and measure their responses. In contrast, a content analysis may not have participants at all, and the researcher may simply read the marketing materials for a corporation or look at a politician’s speeches to conduct the data analysis for the study.

I imagine that a content analysis probably seems easier to accomplish than an experiment. However, as a researcher, you have to choose a research design that makes sense for your question and that is feasible to complete with the resources you have. All research projects require some resources to accomplish. Make sure your design is one you can carry out with the resources (time, money, staff, etc.) that you have.

There are so many different designs that exist in the social science literature that it would be impossible to include them all in this textbook. The purpose of the subsequent chapters is to help you understand the basic designs upon which these more advanced designs are built. As you learn more about research design, you will likely find yourself revising your research question to make sure it fits with the design. At the same time, your research question as it exists now should influence the design you end up choosing. There is no set order in which these should happen. Instead, your research project should be guided by whether you can feasibly carry it out and contribute new and important knowledge to the world.

• Research questions must be feasible and important.
• Research questions must match study design.
• Based on what you know about designs like surveys, experiments, and interviews, describe how you might use one of them to answer your research question.
• You may want to refer back to Chapter 2 which discusses how to get raw data about your topic and the common designs used in student research projects.

Media Attributions

• patrick-starfish-2062906_1920 © Inspired Images is licensed under a CC0 (Creative Commons Zero) license
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• target-group-3460039_1920 © Gerd Altmann is licensed under a CC0 (Creative Commons Zero) license
• Not familiar with SpongeBob SquarePants? You can learn more about him on Nickelodeon’s site dedicated to all things SpongeBob:  http://www.nick.com/spongebob-squarepants/ ↵
• Focus on the Family. (2005, January 26). Focus on SpongeBob.  Christianity Today . Retrieved from  http://www.christianitytoday.com/ct/2005/januaryweb-only/34.0c.html ↵
• BBC News. (2005, January 20). US right attacks SpongeBob video. Retrieved from:  http://news.bbc.co.uk/2/hi/americas/4190699.stm ↵
• In fact, an MA thesis examines representations of gender and relationships in the cartoon: Carter, A. C. (2010).  Constructing gender and   relationships in “SpongeBob SquarePants”: Who lives in a pineapple under the sea . MA thesis, Department of Communication, University of South Alabama, Mobile, AL. ↵

research questions that can be answered by systematically observing the real world

unsuitable research questions which are not answerable by systematic observation of the real world but instead rely on moral or philosophical opinions

the group of people whose needs your study addresses

attempts to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants

"Assuming that the null hypothesis is true and the study is repeated an infinite number times by drawing random samples from the same populations(s), less than 5% of these results will be more extreme than the current result" (Cassidy et al., 2019, p. 233).

whether you can practically and ethically complete the research project you propose

the impact your study will have on participants, communities, scientific knowledge, and social justice

Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• DOI: 10.1111/soc4.13261
• Corpus ID: 271782388

The Importance of Qualitative Methods for Understanding Racialized Injustice and Health

• Karen Lutfey Spencer , Hyeyoung Oh Nelson
• Published in Sociology Compass 1 August 2024

30 References

Sociological contributions to race and health: diversifying the ontological and methodological agenda., structural racism and health stratification: connecting theory to measurement, systemic racism and u.s. health care., misrepresenting race - the role of medical schools in propagating physician bias., improving the measurement of structural racism to achieve antiracist health policy, a matter of time: racialized time and the production of health disparities, data citizenship: quantifying structural racism in covid-19 and beyond, black mothers’ concern for their children as a measure of vicarious racism-related vigilance and allostatic load, racialized burdens: applying racialized organization theory to the administrative state, moving beyond mistrust: centering institutional change by decentering the white analytical lens., related papers.

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• Published: 12 August 2024

Evaluation of didactic units on historical thinking and active methods

• Pedro Miralles-Sánchez   ORCID: orcid.org/0000-0002-2436-3012 1 ,
• Jairo Rodríguez-Medina   ORCID: orcid.org/0000-0002-6466-5525 2 &
• Raquel Sánchez-Ibáñez 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1032 ( 2024 ) Cite this article

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The purpose of this study is to evaluate the effects of an implementation of eight didactic units on historical thinking and active methods as part of a teacher training programme. All this with four specific objectives that try to find out changes in the methodology, motivation, satisfaction and learning of the students. To this end, the research is carried out by means of a mixed method using quantitative data, obtained from a pretest/posttest, and qualitative data, obtained from a focus group and interviews. The target groups of the teaching units are secondary and high school students aged between 13 and 18 years. A total of 114 students of these students participated in the data collection with a pretest/posttest, six master students in the focus group, and three teachers and three secondary and high school students were interviewed. The results obtained indicated that significant differences of medium effect were found in the pre and post phase factor in learning and satisfaction, and of large effect in methodology and motivation. As for the gender factor, significant differences of small effect were found in motivation and satisfaction, with higher values for women. The positive statements of both master’s students and high school students and teachers were quite striking, although the limitations and difficulties must be highlighted. It is concluded that the design of this type of didactic units has meant a significant improvement, achieving that the students have developed a notorious improvement in their perception of the objectives studied.

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Introduction.

Research in history didactics has distinguished two types of historical content. On the one hand, substantive or first-order content. These are those which refer both to concepts or principles and to specific historical dates and events. On the other hand, strategic, second-order content or historical meta-concepts as methodological concepts. These are related to the historian’s skills, the search for, selection and treatment of historical sources, empathy or historical perspective, related to the definition of historical thinking (Sáiz and Gómez, 2016 ). This didactic approach aims for students to learn to think historically by deploying different strategies and competences to analyse and respond to different historical questions and to understand the past in a more complex way. These competences and strategies are related to the search for, selection and treatment of historical sources, empathy, multi-causal explanation, or historical perspective; in short, the functions of a historian (Peck and Seixas, 2008 ; Seixas and Morton, 2013 ). These concepts are variable and do not form a closed and invariable list, but each author gives greater importance to certain aspects (Gómez Carrasco et al., ( 2017 )).

Since the late 1980s, an effort has been made in the British field to analyse second-order concepts in students’ argumentation. Here the Concepts of History and Teaching Approaches project (Lee et al. 1996 ) stands out, which investigated the historical concepts that students should acquire. At the same time, in the USA, through Wineburg ( 2001 ), work began with cognitive psychology techniques (experts and novices) to investigate the skills that students should acquire, with the well-known historical thinking and its competences finally being developed by mainly Canadian and American authors (Ercikan and Seixas, 2015 ; Seixas and Morton, 2013 ; VanSledright, 2014 ; Wineburg et al., 2013 ). For their part, the work of Chapman ( 2011 ) and the Constructing History 11–19 project (Cooper and Chapman, 2009 ) delve deeper into this line of reasoning in the use of sources, a thematic field also addressed in other countries such as the Netherlands (Van Drie and Van Boxtel, 2008 ) and Chile (Henríquez and Ruíz, 2014 ).

The importance of teaching historical thinking in the classroom lies in the fact that historical thinking does not develop naturally, but needs explicit teaching (Wineburg, 2001 ). To develop these competences, the introduction of the historian’s method and techniques and historical awareness are key elements, with appropriate techniques and instruments to assess them (Domínguez, 2015 ). To develop them, a methodological change in the classroom is necessary, as is already being proposed and discussed in countries such as Portugal (Gago, 2018 ), Spain (Navarro and De Alba, 2015 ) or the United Kingdom (Smith, 2019 ). This change implies moving from the current dominance of expository teaching strategies to a greater presence of enquiry strategies that help to promote the development of independence, critical thinking, and autonomous learning in students.

Working with historical sources, which can begin even earlier, is valued positively by students in upper secondary education, as it promotes a research experience in which students construct their knowledge about the past (Prieto, Gómez and Miralles, 2013 ), however, this type of experience is not usually abundant in classrooms at this stage in Spain. The abuse of the lecture and the passive role reserved for students ends up making them, for the most part, limit themselves to studying what is offered in class by not seeking information from other sources and memorising the information they receive (Sáiz and López-Facal, 2015 ). Consequently, it is very difficult to create critical citizenship in students, as they may believe everything the teacher tells them, as they are not familiar with enquiry (Guirao, 2013 ).

When it comes to identifying teaching models, it is worth highlighting the line of research developed by Trigwell and Prosser ( 2004 ) based on interviews with teachers and a questionnaire called Approaches to Teaching Inventory (ATI) (Trigwell et al., 2005 ). They identified four different conceptions of teaching and three methodologies, establishing five approaches which can be grouped into three broad models or ways of teaching. In the first model, the role of the teacher is greater, since the importance lies in the transmission of content, students assume a passive role, limiting themselves to receiving and memorising the knowledge transmitted by teachers, thus establishing a unidirectional relationship, without considering their experience, previous knowledge, characteristics or context. The most used methodological strategy is the master class and the main resources used are the textbook and class notes. In addition, a final examination of the learning contents is usually established (Hernández et al., 2012 ; Guerrero-Romera et al., 2022 ).

On the other hand, there is learner-centred teaching which differs from the previous one in that the teacher’s intention is to provoke conceptual change and intellectual growth in the learner. Thus, the teacher acts as a guide, guiding students in the process of constructing their own knowledge, encouraging their conceptions, and providing them with opportunities to interact, debate, investigate and reflect. The aim of this model is for students to learn content by questioning and reflecting on it. The strategies employed are active and inquiry based. In contrast to the previous model, which encourages competitiveness and individualism, this approach favours interaction and cooperation between the individuals involved in the teaching and learning process and prioritises continuous assessment (Vermunt and Verloop, 1999 ; Kember and Kwan, 2000 ; Trigwell et al., 2005 ; Henze and van Driel, 2011 ). Finally, there is a third, intermediate model based on teacher-student interaction, although it should be noted that there is a hierarchical relationship between the different approaches, with each including elements of the previous one (Guerrero-Romera et al., 2022 ).

Evaluative studies of formative processes such as this one are seeing an increase in the field of history education especially in terms of changing the conceptual model of history teaching (Carretero et al., 2017 ; Metzger and Harris, 2018 ). Some work, such as that being carried out in the Netherlands, focuses on evaluative research that is more focused on teaching practice (De Groot-Reuvekamp et al., 2018 ; Van Straaten et al., 2018 ). Regarding the evaluation of historical thinking effects, we can recently highlight Tirado-Olivares et al. ( 2024 ) relating it to academic performance, or Bartelds et al. ( 2020 ) highlighting the importance of historical empathy. It is also worth highlighting the research carried out by the University of Murcia (Gómez et al., 2021a ; Gómez et al., 2021b ; Rodríguez et al., 2020 ), which implemented training units focused on historical thinking skills and changes in the way of teaching. This research therefore seeks to be a significant improvement compared to traditional methods used in the teaching of social sciences, as it seeks to develop essential skills for critical thinking and citizenship training, and to evaluate its effectiveness through rigorous methods and a scientific approach. All this to encourage a critical spirit and autonomous learning and therefore the formation of critical and independent citizens who know how to judge for themselves the vicissitudes that civic life in democracy demands of them.

The main objective of this article is to detect if there are significant changes in students after the design and implementation of eight didactic units (DU from now on) to promote the learning of historical thinking skills through active teaching methods. To achieve the objective, it has been divided into the following specific objectives:

O1. To analyse whether there are differences in the students’ perception of the methodology of teaching history, after the implementation of the DU that promotes historical thinking through active methods Table 1 .

O2. To identify if there are differences in the students’ perception of motivation during the teaching process, after the implementation of the DU that promote historical thinking through active methods Table 2 .

O3. To find out if there are differences in the students’ perception in relation to the level of satisfaction with the teaching process, after the implementation of the DU that promote historical thinking through active methods Table 3 .

O4. To find out if there are differences in the students’ perception in relation to the level of effectiveness and transfer of the learning achieved, after the implementation of the DU that promote historical thinking through active methods.

Research design

This is an evaluative type of DU research of historical thinking and active methods with a mixed explanatory approach and a quasi-experimental A-B design. The research method is therefore mixed, qualitative, and quantitative data have been collected and analysed in a rigorous way in response to the research objective, organising them into specific research objectives and integrating the two forms of data and their results into conclusions framed in the theory and scientific production studied (Creswell & Plano Clark, 2017 ). The selection of the eight DU was made at random, as we have worked with the students who have been tutored by us during the internship period. On one hand, a quantitative analysis of the data obtained by means of a Likert-type questionnaire (1–5) was carried out. Questionnaire designs are extremely common in the field of education, as they can be applied to a multitude of problems and allow data to be collected on many variables and outcomes to be measured (Sapsford & Jupp, 2006 ). On the other hand, the decision was to apply a qualitative exploratory method through a focus group with master’s students who applied the DU and interviews with practising teachers and students who witnessed these units (supplementary material, Figs. 1 – 3 ). Interviews are useful when you want subjects to describe complex phenomena and facts that are the object of study (Pérez-Juste et al., 2012 ), as well as focus groups. The focus group was recorded via an online Zoom meeting (Archibald et al., 2019 ) and then transcribed using artificial intelligence (Notta AI), while the interviews were answered on the spot individually in writing.

The quantitative analysis (R Core Team, 2023 ), a repeated measures mixed factorial design with one within-subjects factor (the time of assessment) and one between-subjects factor (gender) was used. The within-subject factor has two levels (pretest and posttest) and the between-subject factor has three levels (female and male). The dependent variables were the scores obtained in each of the subscales of the questionnaires Secondary school students’ assessment of History teaching and Secondary school students’ opinion of the implementation of the History training unit (supplementary material Figs. 4 and 5 ). For the qualitative analysis, a descriptive analysis was carried out using the qualitative research software Atlas.Ti 23, which is widely used in research in the field of Social Science Didactics (Rüssen, 1997 ; Sánchez-Ibáñez, Martínez-Nieto ( 2015 )). As a complement to this software, the ChatGPT tool has also been used to improve the accuracy of the codes and data analysis, as an aid both in designing the codes of the transcripts, organising the main conclusions obtained from the coding of the participants’ responses (Lopezosa & Codina, 2023 ), and finding out the percentage of occurrence of words. All codes are open and non-exclusive, so that the same response can be associated with more than one code.

Participants

This is a non-probabilistic convenience sample composed in the quantitative analysis of 114 young people aged between 12 and 20 years (M = 15.63, SD = 1.54). Fifty-one males (44%) and 65 females (56%) participated in the pre-test. In the post-test 50 males (44%) and 64 females (56%) participated. Of these, 14 men and 10 women were from the first year of high school, 5 men and 18 women were from the second year of high school, 11 men and 8 women were from the second year of ESO, 14 men and 21 women from the third year of ESO and 7 men and 10 women from the fourth year of ESO (Fig. 1 ). As for the focus group, 6 students of the master’s degree in teaching, 2 men and 4 women aged between 22–45 years, participated. The interviews were conducted with 3 secondary school teachers, 2 men and 1 woman aged 40–60 and 3 pupils aged 13–17 respectively.

Distribution by Gender and Grade.

Instruments

For the collection of quantitative data, two closed-response questionnaires based on a Likert-type scale (1–5) were used. The questionnaires given to pupils were entitled Assessment of Secondary School pupils on the teaching of History (pretest) and Opinion of Secondary School pupils on the implementation of the History unit (posttest). The questionnaires have 37 items divided into four categories corresponding to each of the specific research objectives: Assessment of the implementation of the DU in the teaching/learning process; Assessment of student motivation in an innovative DU; Analysis of student satisfaction with an innovative DU; Analysis of student learning and its results to check whether the DU has been effective (supplementary material Figs. 4 and 5 ). For its part, the qualitative analysis was used to complement the quantitative research by relating its questions to the objectives and thus elucidating the impact of the OD. It consists of both a focus group with trainee teachers consisting of nine questions and interviews with classroom tutors and students with a total of sixteen questions (supplementary material Figs. 1 – 3 ).

Validation of these instruments has been essential to ensure that the data collected are accurate and reliable, through peer review and pilot testing on a small group of participants to assess the effectiveness and relevance of the questions and observation procedures (Gómez et al., 2021 a; Rodríguez et al., 2020 ; Miralles-Sánchez et al., 2023 ).

This research is based on a research project consisting of four phases: prior observation of the classroom (December 2022-February 2023), design of training units (March-April 2023), implementation of training units (May-July 2023) and evaluation of results (September 2023-July 2024). The design of the DU and the data collection were thanks to a training programme implemented during the academic year 2022/23 in a Spanish university for students of the Master’s degree in teacher training in the speciality of Geography, History and History of Art. Held from 10 January to 17 March 2023, the duration of the activity involved a total of 18 face-to-face hours where students attended a series of lectures given by expert lecturers in Didactics of Social Sciences with the aim of helping students to carry out a Master’s Final Project (MFP) based on the implementation and evaluation of a didactic DU on historical thinking and active methods during the internship period of the Master’s. The activity consisted of 6 sessions: presentation and approach of the MFP, concepts of historical thinking, teaching methods and active evaluation processes, quantitative and qualitative analysis of data in educational research, and guidelines for the presentation and bibliography of the MFP.

O1. To analyse whether there are differences in the students’ perception of the methodology of teaching history, after the implementation of the DU that promotes historical thinking through active methods

In relation to this objective, the data obtained from the quantitative instruments show an approximately normal distribution of methodology scores. No significant differences were observed between sexes (MH = 35.93, SD = 5.60; MM = 36.43, SD = 5.83) in the initial (pre) assessment (F (1,112 = 5.83). 83) at baseline (pre) assessment (F (1,112) = 0.21, p = 0.64) and no gender differences between groups (MH = 43.32, SD = 6.91; MM = 44.53, SD = 7.58) were observed at posttest (F (1,112) = 0.77, p = 0.38).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 0.08, p = 0.77). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 91.88, p < 0.01) with a large effect size (partial η2 = 0.26). Figure 2 shows the result graphically.

Differences in Methodology Scores by Gender and Phase.

The master’s students emphasise that none of them were previously familiar with the theory of historical thinking, having recently learned it in class, although some had experience of teaching with active methods. They emphasise the importance of interactive and participatory methods, as well as the crucial role of the teacher in the educational experience, recognising positive changes in current teaching, although with divergent opinions on the influence of students on methodology. The positive experience with students and the inclusion of relevant points in teaching are highlighted, but the persistence of traditional methods that are not very active and the resistance of some students to participatory methods are criticised, representing a challenge in contemporary teaching Fig. 3 .

Changes and improvements in DU according to master’s students.

Significant statements

“So I think that the figure of the teacher will always be…. All that helps, all the technique, everything we learn and all that, but I think that the figure of the teacher is fundamental, it is important.” - He emphasises the importance of the role of the teacher and the relationship that the teacher establishes with the students.

“I think it’s changing a lot because before you went to class and the teacher would give you a lecture or whatever and the students were very dispersed, but I think that is changing now, and as we bring in new generations, I think it’s going to change a bit more.” - He sees a positive change in the way history teaching is approached.

“No, I think so, in a certain sense it has changed, because it is true that at secondary school, when you are a teenager you see two types of teachers, a teacher who practically limits himself to lecturing you and that’s it, and others who question you more.” - He expresses that teaching has not changed completely, suggesting that there are still teachers who adopt fewer interactive approaches.

“I’ve had bad history teachers all my life, you know, the kind that came in and talked to me unfunnily about things that had happened and that was it.” - Reflects a past negative experience with less committed history teachers.

“So, it’s true that when I was a student, I felt that sometimes history classes were very theoretical and so on, but it’s true that when I came to class as a non-student, I saw that sometimes teachers have to adopt this methodology because otherwise it’s impossible.” - She acknowledges that sometimes teachers are forced to adopt fewer interactive methods due to student resistance.

“My internship tutor said that students are not used to any of this and that in reality many are comfortable in this role of going to the institute like someone who goes to the cinema, to see the teacher or tell the story and then I’ll study and do the exam and that’s it.” - He points to the resistance of some students to more participatory methods as a challenge in today’s teaching.

On the other hand, they stress the crucial role of an active and engaging methodology to enhance the learning experience, with the consideration that there is no single methodology effective for all groups. However, they also mention the importance of dosing or reducing content to avoid information overload, as well as the need for continuous observation and analysis to determine the most effective methods, with a willingness to adapt according to the results. While some participants emphasise the relevance of methodology over content, others argue that both are crucial and should be tailored to each group. In general, there is convergence on the difficulty in achieving active student participation, attributing this to a lack of empathy or resistance towards interactive activities, recognising the importance of adapting methodologies to the needs of each group and constantly evaluating their effectiveness. The need to simplify teaching and focus on relevant aspects of the curriculum is mentioned, as well as the need to face technological challenges with alternative plans. Their commitment to quality teaching, willingness to learn and adapt is also highlighted, although areas for improvement such as more detailed planning, time and classroom management are mentioned.

Literal and derived mentions of relevant words in the code “Changes and improvements in interventions”: Methodology: 34 times (5.53%), Activities: 21 times (3.43%), Technology: 21 times (3.43%), Content: 18 times (2.94%), Plan: 10 times (1.63%), Topic: 6 times (0.98%), Participate: 6 times (0.98%), Exam: 5 times (0.82%), Adapt: 5 times (0.82%).

As far as secondary school students are concerned, in general, there is a diversity of opinions among students regarding the methodology of teaching history. Some prefer more dynamic and visual approaches, while others are happy with the traditional way of teaching. The perception of motivation also highlights the importance of active participation and discussion in the learning process. This variability may be attributable to personal experiences, levels of interest in the subject or perceptions about the purpose of history education. To gain a deeper understanding, it would be useful to further explore the reasons behind students’ responses. Students’ ratings of the current teacher’s experience suggest that teaching experience and ability are considered important factors in teaching effectiveness.

While Teacher 1 and Teacher 3 recognise aspects of the competence-based approach to historical thinking in teaching practice, Teacher 2 is not familiar with the specific term. Regarding the development of historical competences in pupils, Teacher 1 highlights the importance of adapting materials to children’s understanding from an early age, while Teacher 2 suggests interdepartmental collaboration and family involvement to improve outcomes. Teacher 3 recognises the need for continuous improvement and stresses the importance of learning from mistakes. In relation to teaching perspectives and approaches, Teacher 3 emphasises the connection between historical events and social, economic and political contexts over time, highlighting the importance of ‘historical empathy’. Finally, teachers agree on the challenges and complexities of teaching historical competences, highlighting the need to make them understandable for students and to avoid reducing them to mere memorisation.

Regarding active learning methodologies such as project or problem-based learning, there are differences in its implementation between Teacher 1, who uses it more in lower grades due to exam preparation, and Teacher 2, who offers a short answer. Teacher 3 shows experience in educational innovation projects, indicating a predisposition towards more innovative approaches. The commitment and dedication required is highlighted, as well as the lack of detail on implementation by Teacher 1, which may limit its wider application due to the associated stress and workload. Several challenges and limitations in the implementation of active teaching methodologies are highlighted. These challenges include existing workload, loneliness among colleagues, lack of digital resources both at school and at home for students, limited time in the classroom, language barrier in understanding concepts, lack of teacher training, distrust of new methodologies, and the complexity of catering for diversity in the classroom. In addition, it is stressed that the impact of the methodology on student learning requires adequate assessment and collaborative work to generate significant changes.

Finally, it should be noted that the three teachers agree that active methodologies and historical thinking are not widespread in secondary classrooms. The reasons mainly point to lack of training, time constraints, lack of resources and mistrust on the part of teachers. Inertia in the education system, resistance to changing traditional pedagogical practices and a preference for safe and rote approaches are also mentioned. We can see that resistance to change seems to be a significant barrier. Lack of training and institutional support is highlighted as a key problem. The importance of satisfying studious learners through traditional methods is mentioned as a potential barrier to adopting more creative and reflective approaches.

O2. To identify if there are differences in the students’ perception of motivation during the teaching process, after the implementation of the DU that promote historical thinking through active methods

In relation to this objective, the data obtained from the quantitative instruments show an approximately normal distribution of the motivation scores. No significant differences were observed between sexes (MH = 22.45, SD = 4.86; MM = 23.33 SD = 5.40) in the initial (pre) assessment (F (1,112) = 0.82, p  = 0.36). However, significant differences were observed at the posttest as a function of gender (MH = 25.94, SD = 5.85; MM = 28.33, SD = 5.27) (F (1,112) = 5.26, p  < 0.05) with a small effect size (partial η2 = . Significant differences were observed in the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06).

Repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 1.08, p  = 0.30). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 48.83, p < 0.01) with a large effect size (η2 = 0.144). Similarly, a significant effect of the Sex factor (F (1,108) = 4.63, p  = 0.30) with a small effect size (partial η2 = 0.026) was observed. Figure 4 shows the result graphically. Therefore, motivation increased in both groups after the intervention, but especially in the female group.

Differences in Motivation Scores by Gender and Phase.

Master students highlight a higher motivation (8 positive occurrences in the code “Improvements and difficulties in the DU” 1.23%) and satisfaction (4 positive occurrences in this code 0.61%) among students despite facing difficulties. Some participants noted an improvement in their teaching skills after applying the DU, highlighting the importance of practical experience and the application of theoretical concepts in lesson planning and execution. The implementation of gamification and flipped classroom was mentioned to make teaching more attractive, showing the ability to adapt to challenging situations and look for alternative solutions. The importance of the teacher in the learning experience was highlighted and difficulties related to the implementation of technology in the classroom and the resistance of some students to participate in interactive activities were pointed out.

“Overall it did increase a lot of satisfaction and their motivation regarding the subject.”

“In general what I planned worked and it worked more than anything else in the time I had planned.”

“Well, I think that yes, it worked for them, that it was something they had never given before and it was totally different and they liked it.”

“I mean, yes there are digital whiteboards, yes there are projectors, but it’s complicated, especially to apply, in this case, a didactic unit.”

“So, the cooperative work part is fine, the inverted classroom, fatal.”

“But I also think that it was more or less the same as what they were doing with their teacher.”

“But yes, on the days when they were in the classroom, it was more or less the same as what they were doing with their teacher.”

“But yes, on the days when it was two hours, it was noticeable because just before break time I was already tired”.

On the other hand, in general, the perception of the secondary school students interviewed on the effectiveness of the trainee teachers’ teaching method is ambiguous and could benefit from more specific details on the perceived changes. As an analysis we can indicate that the introduction of these DU seems to have had a positive impact on students’ attention and motivation, the use of audio-visual methods and interactivity are prominent aspects of the new methodology that students appreciate. The relationship between the way of teaching and the retention of information for exams is highlighted as an important point for student satisfaction, and resources such as slides, and short videos are specific elements that students find useful. Therefore, the new way of working of the trainee teacher seems to have generated a positive experience for the students, improving participation, motivation, and information retention.

Teachers in this regard highlight positive results, such as improved motivation and reduced student boredom, as well as increased class participation. However, they recognise that the effectiveness of techniques may vary and that training in new active learning methodologies is needed to address student diversity and to keep up to date. In addition, they highlight a shift towards a more active and participatory approach to learning, which can benefit the development of critical skills and student engagement. The importance of adaptability of methodologies is emphasised, as their effectiveness depends on factors such as the subject matter, the group of learners and the resources available. It is pointed out that student motivation can influence their adaptation to the methodologies, and the use of visual and playful techniques to engage less motivated students is suggested. In addition, it is emphasised that the aim of teaching history is to enable students to interpret the world today, thus encouraging critical thinking. The effectiveness of diversity intervention programmes is acknowledged, highlighting the importance of making the content relevant to each learner.

O3. To find out if there are differences in the students’ perception in relation to the level of satisfaction with the teaching process, after the implementation of the DU that promote historical thinking through active methods

An approximately normal distribution of satisfaction scores is observed. No significant differences were observed between sexes (MH = 21.98, SD = 3.72; MM = 22.13 SD = 3.43) in the initial (pre) assessment (F (1,112) = 0.05, p  = 0.83). However, significant differences were observed at the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 3.04, p  = 0.08). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 51.6, p  < 0.01) with a medium effect size (η2 = 0.13). That is, the intervention had a significant effect on students’ satisfaction with the subject. Figure 5 shows the result graphically.

Differences in Satisfaction Scores by Gender and Stage.

As a general observation we can indicate that all three secondary school pupils interviewed have positive perceptions of the usefulness of history. The definitions of history are varied, but they share the central idea of past events, and the pupils’ responses show a basic understanding of the importance of history in understanding the present and developing critical skills. Their interest in learning about the past is highlighted and it is noted that the content of lessons and the amount of work for exams are important considerations for some students. Students’ comments suggest that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and understanding of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns and improve student motivation. It would be beneficial to delve deeper into the responses to better understand the underlying reasons behind their perceptions and to gain a more complete picture of their experience with the subject.

O4. To find out if there are differences in the students’ perception in relation to the level of effectiveness and transfer of the learning achieved, after the implementation of the DU that promote historical thinking through active methods

An approximately normal distribution of perceived learning scores is observed. Table 4 presents the results for perceived learning on a scale of 13 to 65. No significant gender differences were observed (MH = 40.27, SD = 5.40; MM = 40.67, SD = 5.14) at the initial (pre) assessment (F (1,112) = 0.16, p  = 0.69). There were also no significant sex differences at posttest (MH = 43.94, SD = 6.32; MM = 45.39, SD = 6.38) (F (1,112) = 1.46, p  = 0.23).

The repeated measures analysis of variance did not produce a significant interaction effect result between sex (Female, Male) and phase (Pre vs Post) (F (1,108) = 0.82, p  = 0.37). However, a significant effect of the phase (Pre vs Post) factor was observed (F (1,108) = 52.71 p  < 0.01) with a medium effect size (η2 = 0.12). That is, the intervention had a significant effect on students’ perception of learning. Fig. 6 shows the result graphically.

Differences in Perceived Learning Scores by Gender and Stage.

Master’s students recognise the usefulness of the theory of historical thinking in the planning and execution of classes, as well as the importance of the ethical dimension of history and the need to connect history with citizenship education. The use of primary sources and active methodology to involve students in historical analysis is highlighted. Furthermore, the importance of contextualising history teaching in the immediate environment and addressing social, cultural, and political issues to develop critical thinking in students is emphasised. However, there are divergences among the participants in terms of the perceived novelty of the theory of historical thinking, the depth of ethical exploration in the historical context and the inclusion of themes. Finally, the importance of connecting history with current affairs is mentioned, although this may present challenges in the handling of sensitivities and emotions during the teaching of certain historical topics.

For their part, teachers seem to agree that history teaching should not be limited to the transmission of historical facts, but should also encourage critical thinking, reflection and active participation in social problems. Citizenship education is seen as a process that goes beyond the acquisition of knowledge, including the development of analytical skills and the ability to question and criticise social and political reality.

Discussion and conclusions

If we look at the first objective, we can see that a significant effect of the phase factor (Pre vs Post) was observed in the methodology (F (1,108) = 91.88, p  < 0.01) with a large effect size (partial η2 = 0.26). In turn, we can see corroboration of this change as master’s students highlight in their statements the importance of interactive and participatory methods, as well as the role of the teacher in the educational experience. They recognise positive changes in current teaching, highlighting the positive experience with children and the inclusion of relevant points, but they criticise the persistence of traditional methods that are not very active and the resistance of some students to participatory methods. This represents a challenge in contemporary teaching, with difficulties in achieving active student participation attributed to a lack of empathy or resistance to interactive activities. The importance of adapting methodologies to the needs of each group and constantly evaluating their effectiveness is therefore highlighted, although some also point out the need to dose the content and adapt according to the results.

For their part, high school students emphasise the importance of visual resources, discussions and the connection between past and present in history teaching, as well as teaching experience and skill, reflecting diversity in preferences and learning styles. The effectiveness of the trainee teachers’ teaching methods is ambiguously perceived and may need more specific details on perceived changes. On the other hand, high school teachers recognise the need for training in new methodologies to address student diversity and to keep up to date, highlighting a shift towards a more active and participatory approach to learning. This coincides with the results of Sánchez et al. ( 2020 ) where they note an advance in teachers’ perception of a methodology oriented towards fostering historical and critical thinking in students. However, these teachers face various difficulties and limitations in the implementation of these methodologies, such as workload, lack of digital resources and the language barrier. The impact of the methodologies on learning requires adequate assessment and collaborative work to generate significant changes, being one of the main challenges for education in the future. Consequently, we believe it is crucial that educational administrations encourage the motivation and training of both new and old teachers in order to achieve the necessary methodological improvement in the teaching of history. Teachers suggested that the use of visual and playful techniques engage less motivated students, and the aim of fostering critical thinking through history teaching is highlighted, so the effectiveness of the intervention programmes for diversity is recognised, emphasising the relevance of the content for each student.

This may lead us to see that the generalised perception of students in the pre-test denotes the persistence of the traditional teaching model with the absence of active methods, digital resources, and historical thinking skills. Monteagudo-Fernández et al. ( 2020 ) obtain similar results in a study with secondary education and baccalaureate students, confirming the existence of a traditional model in the teaching of history that excludes cooperative and inquiry-based methodologies. This reality must point towards a didactic model that prioritises competence learning and student activism in their learning process, highlighting advocates such as Carretero et al., ( 2017 ) or Metzger & Harris, ( 2018 ), who are committed to a methodological change that moves away from the predominant conceptual model for teaching history.

In terms of motivation, we can see that a significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 48.83, p  < 0.01) with a large effect size (η2 = 0.144). Similarly, a significant effect of the Sex factor (F (1,108) = 4.63, p  = 0.30) with a small effect size (partial η2 = 0.026) was observed. Thus, motivation increased in both groups after the intervention, but especially in the female group. The master’s students corroborate this by highlighting a higher motivation and satisfaction among students despite facing difficulties, while for high school students, in general, the new way of working of the trainee teacher seems to have generated a positive experience, improving participation, motivation and retention of information. The importance of active participation and discussion in the learning process is particularly emphasised by the high school students. Teachers highlight positive results, such as improved motivation and reduced student boredom, as well as increased participation in class. However, there is no significant statement regarding a difference in motivation with respect to gender, which may suggest that this is a change that is little perceived by teachers and students, but which is present and should be considered when applying these active and historical thinking methods.

These results are similar to those presented by several authors (Gómez et al., 2021a ; Gómez et al., 2021b ; Rodríguez et al., 2020 ), who also highlight as the most important factor that motivation is due to the use of resources other than the school textbook, which is very good news for continuing to take steps towards methodological complementarity, so that the students themselves are aware that by using all kinds of resources to learn, they can and should be more motivated. In these studies (Gómez et al., 2021a ; Gómez et al., 2021b ), they also found that the item with the lowest score in their pretest is the one that states that students are motivated because they can contribute their points of view and knowledge, something that clearly does not occur in traditional classes where the students’ role as receivers predominates. For his part, Singer ( 1996 ) considers gender to be one of the most significant predictors in relation to teaching approaches. In this sense, Maquilón, Sánchez and Cuesta ( 2016 ), in their study of active Primary School teachers, point out that men tend to opt for an approach based on the transmission and reproduction of information, while women are inclined towards a more student-centred approach.

In satisfaction, significant differences were also observed in the posttest as a function of gender (MH = 23.94, SD = 3.95; MM = 25.75, SD = 3.24) (F (1,112) = 7.23, p  < 0.05) with a small effect size (partial η2 = 0.06), as for motivation (MH = 25.94, SD = 5.85; MM = 28.33, SD = 5.27) (F (1,112) = 5.26, p  < 0.05) (partial η2 = 0.04). However, repeated measures analysis of variance did not produce a significant result of interaction effect between sex and phase (F (1,108) = 3.04, p  = 0.08). A significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 51.6, p  < 0.01) with a medium effect size (η2 = 0.13). In other words, the intervention had a significant effect on students’ satisfaction with the subject, in agreement with what was stated by the master’s students and teaching staff on the improvement of student motivation and satisfaction. They highlight the relationship between the way of teaching and the retention of information for the exams as an important point for their satisfaction. High school students highlight that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and comprehension of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns and improve pupils’ motivation.

Finally, on learning, a significant effect of the phase factor (Pre vs Post) was observed (F (1,108) = 52.71 p  < 0.01) with a medium effect size (η2 = 0.12). That is, the intervention had a significant effect on students’ perception of learning. Master’s students highlight the importance of the teacher in the learning experience and difficulties related to the implementation of technology in the classroom and the reluctance of some students to participate in interactive activities were noted, although the crucial role of this methodology in enhancing the learning experience is highlighted, with the consideration that there is no single methodology effective for all groups. Students suggest that there are aspects of history teaching that could be improved, such as the presentation of information, the length of language and the possible lack of connection between memorisation and understanding of content. Diversifying teaching methods and incorporating more dynamic approaches could help to address these concerns. Teachers for their part highlight the shift towards a more active and participatory approach to learning, which can benefit the development of critical skills and student engagement. However, this requires adequate assessments and collaborative work to generate significant changes, as well as continuous training in active learning methodologies and strategies, considered essential nowadays.

There is still an overuse of textbooks and the expository strategy by teachers who teach History (Carretero and Van Alphen, 2014 ; Colomer et al., 2018 ). However, more and more teachers in Spain are in favour of a teaching model in which the student acquires a greater role through the implementation of innovative resources (heritage, written and oral sources, new technologies) and educational strategies that encourage the active participation of students in the teaching and learning process (project-based learning, gamification, flipped classroom) (Gómez et al., 2018 ; Gómez et al., 2021a ; Sánchez et al., 2020 ). It is therefore important to be aware of developments in the incorporation of competence-based social sciences teaching and a learner-centred model at all levels of education.

We can conclude from the above that the programme was quite effective in the objectives studied. In the quantitative data we observed an improvement in the students’ perception of all the variables studied after the intervention, especially the change in methodology and the improvement in motivation had a large effect size. Moreover, it can be noted that the DOMs applied most of the methods, techniques, and resources we proposed in the training programme (supplementary material Fig. 6 ). On the other hand, we found quite positive statements about the programme from both master’s students and high school students and teachers as we have seen in the different points. However, it is important to point out the limitations and difficulties reported by teachers and students when implementing this type of unit, as well as the fact that there were some weaknesses in this study, such as the small quantitative and qualitative sample group. As a possible future improvement when carrying out the interviews and organising the focus group, it is possible to point out that it could be organised with more time and written commitment from the participants, as the initial intention was for 8 teachers, secondary school students and Master’s students to participate, respectively, one for each unit applied. The limitations of their availability played a negative role in the collection of more qualitative data, as participation was voluntary and, in the case of high school students, parental approval was required.

Data availability

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

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RSI and JR-M: conceived and designed the project and doctoral thesis of which this study is part. PMS and JR-M.: have made methodology, data collection and formal analysis. PM-S and JR-M have co-written the manuscript and RSI contributed to revisions, having read and approved the submitted manuscript. All authors have read and agreed to the published version of the manuscript.

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This study was performed in line with the principles of the Declaration of Helsinki. It is part of grant PRE2021-097619, funded by MCIN/AEI/10.13039/501100011033 and ESF + . It is part of the research project “La enseñanza y el aprendizaje de competencias históricas en bachillerato: un reto para lograr una ciudadanía crítica y democrática” (PID2020-113453RB-I00), funded by the Agencia Estatal de Investigación (AEI/10.13039/501100011033). This project was granted favourable by Ethics Research Committee of the University of Murcia 8/03/2021.

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Miralles-Sánchez, P., Rodríguez-Medina, J. & Sánchez-Ibáñez, R. Evaluation of didactic units on historical thinking and active methods. Humanit Soc Sci Commun 11 , 1032 (2024). https://doi.org/10.1057/s41599-024-03546-9

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How can health systems approach reducing health inequalities? An in-depth qualitative case study in the UK

• Charlotte Parbery-Clark 1 ,
• Lorraine McSweeney 2 ,
• Joanne Lally 3 &
• Sarah Sowden 4  

BMC Public Health volume  24 , Article number:  2168 ( 2024 ) Cite this article

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Addressing socioeconomic inequalities in health and healthcare, and reducing avoidable hospital admissions requires integrated strategy and complex intervention across health systems. However, the understanding of how to create effective systems to reduce socio-economic inequalities in health and healthcare is limited. The aim was to explore and develop a system’s level understanding of how local areas address health inequalities with a focus on avoidable emergency admissions.

In-depth case study using qualitative investigation (documentary analysis and key informant interviews) in an urban UK local authority. Interviewees were identified using snowball sampling. Documents were retrieved via key informants and web searches of relevant organisations. Interviews and documents were analysed independently based on a thematic analysis approach.

Interviews ( n  = 14) with wide representation from local authority ( n  = 8), NHS ( n  = 5) and voluntary, community and social enterprise (VCSE) sector ( n  = 1) with 75 documents (including from NHS, local authority, VCSE) were included. Cross-referenced themes were understanding the local context, facilitators of how to tackle health inequalities: the assets, and emerging risks and concerns. Addressing health inequalities in avoidable admissions per se was not often explicitly linked by either the interviews or documents and is not yet embedded into practice. However, a strong coherent strategic integrated population health management plan with a system’s approach to reducing health inequalities was evident as was collective action and involving people, with links to a “strong third sector”. Challenges reported include structural barriers and threats, the analysis and accessibility of data as well as ongoing pressures on the health and care system.

We provide an in-depth exploration of how a local area is working to address health and care inequalities. Key elements of this system’s working include fostering strategic coherence, cross-agency working, and community-asset based approaches. Areas requiring action included data sharing challenges across organisations and analytical capacity to assist endeavours to reduce health and care inequalities. Other areas were around the resilience of the system including the recruitment and retention of the workforce. More action is required to embed reducing health inequalities in avoidable admissions explicitly in local areas with inaction risking widening the health gap.

Highlights:

• Reducing health inequalities in avoidable hospital admissions is yet to be explicitly linked in practice and is an important area to address.

• Understanding the local context helps to identify existing assets and threats including the leverage points for action.

• Requiring action includes building the resilience of our complex systems by addressing structural barriers and threats as well as supporting the workforce (training and wellbeing with improved retention and recruitment) in addition to the analysis and accessibility of data across the system.

Peer Review reports

Introduction

The health of our population is determined by the complex interaction of several factors which are either non-modifiable (such as age, genetics) or modifiable (such as the environment, social, economic conditions in which we live, our behaviours as well as our access to healthcare and its quality) [ 1 ]. Health inequalities are the avoidable and unfair systematic differences in health and healthcare across different population groups explained by the differences in distribution of power, wealth and resources which drive the conditions of daily life [ 2 , 3 ]. Essentially, health inequalities arise due to the systematic differences of the factors that influence our health. To effectively deal with most public health challenges, including reducing health inequalities and improving population health, broader integrated approaches [ 4 ] and an emphasis on systems is required [ 5 , 6 ] . A system is defined as ‘the set of actors, activities, and settings that are directly or indirectly perceived to have influence in or be affected by a given problem situation’ (p.198) [ 7 ]. In this case, the ‘given problem situation' is reducing health inequalities with a focus on avoidable admissions. Therefore, we must consider health systems, which are the organisations, resources and people aiming to improve or maintain health [ 8 , 9 ] of which health services provision is an aspect. In this study, the system considers NHS bodies, Integrated Care Systems, Local Authority departments, and the voluntary and community sector in a UK region.

A plethora of theories [ 10 ], recommended policies [ 3 , 11 , 12 , 13 ], frameworks [ 1 , 14 , 15 ], and tools [ 16 ] exist to help understand the existence of health inequalities as well as provide suggestions for improvement. However, it is reported that healthcare leaders feel under-skilled to reduce health inequalities [ 17 ]. A lack of clarity exists on how to achieve a system’s multi-agency coherence to reduce health inequalities systematically [ 17 , 18 ]. This is despite some countries having legal obligations to have a regard to the need to attend to health and healthcare inequalities. For example, the Health and Social Care Act 2012 [ 19 ], in England, mandated Clinical Commissioning Groups (CCGs), now transferred to Integrated Care Boards (ICBs) [ 20 ], to ‘have a regard to the need to reduce inequalities between patients with respect to their ability to access health services, and reduce inequalities between patients with respect to the outcomes achieved for them by the provision of health services’. The wider determinants of health must also be considered. For example, local areas have a mandatory requirement to have a joint strategic needs assessment (JSNA) and joint health and wellbeing strategy (JHWS) whose purpose is to ‘improve the health and wellbeing of the local community and reduce inequalities for all ages' [ 21 ] This includes addressing the wider determinants of health [ 21 ]. Furthermore, the hospital care costs to the NHS associated with socioeconomic inequalities has been previously reported at £4.8 billion a year due to excess hospitalisations [ 22 ]. Avoidable emergency admissions are admissions into hospital that are considered to be preventable with high-quality ambulatory care [ 23 ]. Both ambulatory care sensitive conditions (where effective personalised care based in the community can aid the prevention of needing an admission) and urgent care sensitive conditions (where a system on the whole should be able to treat and manage without an admission) are considered within this definition [ 24 ] (encompassing more than 100 International Classification of Diseases (ICD) codes). The disease burden sits disproportionately with our most disadvantaged communities, therefore highlighting the importance of addressing inequalities in hospital pressures in a concerted manner [ 25 , 26 ].

Research examining one component of an intervention, or even one part of the system, [ 27 ] or which uses specific research techniques to control for the system’s context [ 28 ] are considered as having limited use for identifying the key ingredients to achieve better population health and wellbeing [ 5 , 28 ]. Instead, systems thinking considers how the system’s components and sub-components interconnect and interrelate within and between each other (and indeed other systems) to gain an understanding of the mechanisms by which things work [ 29 , 30 ]. Complex interventions or work programmes may perform differently in varying contexts and through different mechanisms, and therefore cannot simply be replicated from one context to another to automatically achieve the same outcomes. Ensuring that research into systems and systems thinking considers real-world context, such as where individuals live, where policies are created and interventions are delivered, is vital [ 5 ]. How the context and implementation of complex or even simple interventions interact is viewed as becoming increasingly important [ 31 , 32 ]. Case study research methodology is founded on the ‘in-depth exploration of complex phenomena in their natural, or ‘real-life’, settings’ (p.2) [ 33 ]. Case study approaches can deepen the understanding of complexity addressing the ‘how’, ‘what’ and ‘why’ questions in a real-life context [ 34 ]. Researchers have highlighted the importance of engaging more deeply with case-based study methodology [ 31 , 33 ]. Previous case study research has shown promise [ 35 ] which we build on by exploring a systems lens to consider the local area’s context [ 16 ] within which the work is implemented. By using case-study methodology, our study aimed to explore and develop an in-depth understanding of how a local area addresses health inequalities, with a focus on avoidable hospital admissions. As part of this, systems processes were included.

Study design

This in-depth case study is part of an ongoing larger multiple (collective [ 36 ]) case study approach. An instrumental approach [ 34 ] was taken allowing an in-depth investigation of an issue, event or phenomenon, in its natural real-life context; referred to as a ‘naturalistic’ design [ 34 ]. Ethics approval was obtained by Newcastle University’s Ethics Committee (ref 13633/2020).

Study selection

This case study, alongside the other three cases, was purposively [ 36 ] chosen considering overall deprivation level of the area (Indices of Multiple Deprivation (IMD) [ 37 ]), their urban/rural location, differing geographical spread across the UK (highlighted in patient and public feedback and important for considering the North/South health divide [ 38 ]), and a pragmatic judgement of likely ability to achieve the depth of insight required [ 39 ]. In this paper, we report the findings from one of the case studies, an urban local authority in the Northern region of the UK with high levels of socioeconomic disadvantage. This area was chosen for this in-depth case analysis due to high-level of need, and prior to the COVID-19 pandemic (2009-2018) had experienced a trend towards reducing socioeconomic inequalities in avoidable hospital admission rates between neighbourhoods within the local area [ 40 ]. Thereby this case study represents an ‘unusual’ case [ 41 ] to facilitate learning regarding what is reported and considered to be the key elements required to reduce health inequalities, including inequalities in avoidable admissions, in a local area.

Semi-structured interviews

The key informants were identified iteratively through the documentary analysis and in consultation with the research advisory group. Initially board level committee members (including lay, managerial, and clinical members) within relevant local organisations were purposively identified. These individuals were systems leaders charged with the remit of tackling health inequalities and therefore well placed to identify both key personnel and documents. Snowball sampling [ 42 ] was undertaken thereafter whereby interviewees helped to identify additional key informants within the local system who were working on health inequalities, including avoidable emergency admissions, at a systems level. Interview questions were based on an iteratively developed topic guide (supplementary data 1), informed from previous work’s findings [ 43 ] and the research advisory network’s input. A study information sheet was emailed to perspective interviewees, and participants were asked to complete an e-consent form using Microsoft Forms [ 42 ]. Each interviewee was interviewed by either L.M. or C.P.-C. using the online platforms Zoom or Teams, and lasted up to one hour. Participants were informed of interviewers’ role, workplace as well as purpose of the study. Interviewees were asked a range of questions including any work relating to reducing health inequalities, particularly avoidable emergency admissions, within the last 5 years. Brief notes were taken, and the interviews were recorded, transcribed verbatim and anonymised.

Documentary analysis

The documentary analysis followed the READ approach [ 44 ]. Any documents from the relevant local/regional area with sections addressing health inequalities and/or avoidable emergency admissions, either explicitly stated or implicitly inferred, were included. A list of core documents was chosen, including the local Health and Wellbeing Strategy (Table 1 ). Subsequently, other documents were identified by snowballing from these core documents and identification by the interviewees. All document types were within scope if produced/covered a period within 5 years (2017-2022), including documents in the public domain or not as well as documents pertaining to either a regional, local and neighbourhood level. This 5-year period was a pragmatic decision in line with the interviews and considered to be a balance of legacy and relevance. Attempts were made to include the final version of each document, where possible/applicable, otherwise the most up-to-date version or version available was used.

An Excel spreadsheet data extraction tool was adapted with a priori criteria [ 44 ] to extract the data. This tool included contextual information (such as authors, target area and document’s purpose). Also, information based on previous research on addressing socioeconomic inequalities in avoidable emergency admissions, such as who stands to benefit, was extracted [ 43 ]. Additionally, all documents were summarised according to a template designed according to the research’s aims. Data extraction and summaries were undertaken by L.M. and C.P.-C. A selection was doubled coded to enhance validity and any discrepancies were resolved by discussion.

Interviews and documents were coded and analysed independently based on a thematic analysis approach [ 45 ], managed by NVivo software. A combination of ‘interpretive’ and ‘positivist’ stance [ 34 , 46 ] was taken which involved understanding meanings/contexts and processes as perceived from different perspectives (interviewees and documents). This allowed for an understanding of individual and shared social meanings/reasonings [ 34 , 36 ]. For the documentary analysis, a combination of both content and thematic analysis as described by Bowen [ 47 ] informed by Braun and Clarke’s approach to thematic analysis [ 45 ] was used. This type of content analysis does not include the typical quantification but rather a review of the document for pertinent and meaningful passages of text/other data [ 47 ]. Both an inductive and deductive approach for the documentary analysis’ coding [ 46 , 47 ] was chosen. The inductive approach was developed a posteriori; the deductive codes being informed by the interviews and previous findings from research addressing socioeconomic inequalities in avoidable emergency admissions [ 43 ]. In line with qualitative epistemological approach to enquiry, the interview and documentary findings were viewed as ‘truths’ in themselves with the acceptance that multiple realities can co-exist [ 48 ]. The analysis of each set of themes (with subthemes) from the documentary analysis and interviews were cross-referenced and integrated with each other to provide a cohesive in-depth analysis [ 49 ] by generating thematic maps to explore the relationships between the themes. The codes, themes and thematic maps were peer-reviewed continually with regular meetings between L.M., C.P.-C., J.L. and S.S. Direct quotes are provided from the interviews and documentary analysis. Some quotes from the documents are paraphrased to protect anonymity of the case study after following a set process considering a range of options. This involved searching each quote from the documentary analysis in Google and if the quote was found in the first page of the result, we shortened extracts and repeated the process. Where the shortened extracts were still identifiable, we were required to paraphrase that quote. Each paraphrased quote and original was shared and agreed with all the authors reducing the likelihood of inadvertently misinterpreting or misquoting. Where multiple components over large bodies of text were present in the documents, models were used to evidence the broadness, for example, using Dahlgren’s and Whitehead’s model of health determinants [ 1 ]. Due to the nature of the study, transcripts and findings were not shared with participants for checking but will be shared in a dissemination workshop in 2024.

Patient and public involvement and engagement

Four public contributors from the National Institute for Health and Care Research (NIHR) Research Design Service (RDS) North East and North Cumbria (NENC) Public and Patient Involvement (PPI) panel have been actively engaged in this research from its inception. They have been part of the research advisory group along with professional stakeholders and were involved in the identification of the sampling frame’s key criteria. Furthermore, a diverse group of public contributors has been actively involved in other parts of the project including developing the moral argument around action by producing a public facing resource exploring what health inequalities mean to people and public views of possible solutions [ 50 ].

Semi-structured interviews: description

Sixteen participants working in health or social care, identified through the documentary analysis or snowballing, were contacted for interview; fourteen consented to participate. No further interviews were sought as data sufficiency was reached whereby no new information or themes were being identified. Participant roles were broken down by NHS ( n  = 5), local authority/council ( n  = 8), and voluntary, community and social enterprise (VSCE) ( n  = 1). To protect the participants’ anonymity, their employment titles/status are not disclosed. However, a broad spectrum of interviewees with varying roles from senior health system leadership (including strategic and commissioner roles) to roles within provider organisations and the VSCE sector were included.

Documentary analysis: description

75 documents were reviewed with documents considering regional ( n  = 20), local ( n  = 64) or neighbourhood ( n  = 2) area with some documents covering two or more areas. Table 2 summarises the respective number of each document type which included statutory documents to websites from across the system (NHS, local government and VSCE). 45 documents were named by interviewees and 42 documents were identified as either a core document or through snowballing from other documents. Of these, 12 documents were identified from both. The timescales of the documents varied and where possible to identify, was from 2014 to 2031.

Integrative analysis of the documentary analysis and interviews

The overarching themes encompass:

Understanding the local context

Facilitators to tacking health inequalities: the assets

Emerging risks and concerns

Figure 1 demonstrates the relationships between the main themes identified from the analysis for tackling health inequalities and improving health in this case study.

Diagram of the relationship between the key themes identified regarding tackling health inequalities and improving health in a local area informed by 2 previous work [ 14 , 51 ]. NCDs = non-communicable diseases; HI = health inequalities

Understanding the local context was discussed extensively in both the documents and the interviews. This was informed by local intelligence and data that was routinely collected, monitored, and analysed to help understand the local context and where inequalities lie. More bespoke, in-depth collection and analysis were also described to get a better understanding of the situation. This not only took the form of quantitative but also considered qualitative data with lived experience:

‛So, our data comes from going out to talk to people. I mean, yes, especially the voice of inequalities, those traditional mechanisms, like surveys, don't really work. And it's about going out to communities, linking in with third sector organisations, going out to communities, and just going out to listen…I think the more we can bring out those real stories. I mean, we find quotes really, really powerful in terms of helping people understand what it is that matters.’ (LP16).

However, there were limitations to the available data including the quality as well as having enough time to do the analysis justice. This resulted in difficulties in being able to fully understand the context to help identify and act on the required improvements.

‘A lack of available data means we cannot quantify the total number of vulnerable migrants in [region]’ (Document V).

‛So there’s lots of data. The issue is joining that data up and analysing it, and making sense of it. That’s where we don’t have the capacity.’ (LP15).

Despite the caveats, understanding the context and its data limitations were important to inform local priorities and approaches on tackling health inequalities. This understanding was underpinned by three subthemes which were understanding:

the population’s needs including identification of people at higher risk of worse health and health inequalities

the driving forces of those needs with acknowledgement of the impact of the wider determinants of health

the threats and barriers to physical and mental health, as well as wellbeing

Firstly, the population’s needs, including identification of people at higher risk of worse health and health inequalities, was important. This included considering risk factors, such as smoking, specific groups of people and who was presenting with which conditions. Between the interviews and documents, variation was seen between groups deemed at-risk or high-risk with the documents identifying a wider range. The groups identified across both included marginalised communities, such as ethnic minority groups, gypsy and travellers, refugees and asylum seekers as well as people/children living in disadvantaged area.

‘There are significant health inequalities in children with asthma between deprived and more affluent areas, and this is reflected in A&E admissions.' (Document J).

Secondly, the driving forces of those needs with acknowledgement of the impact of the wider determinants of health were described. These forces mapped onto Dahlgren’s and Whitehead’s model of health determinants [ 1 ] consisting of individual lifestyle factors, social and community networks, living and working conditions (which include access to health care services) as well as general socio-economic, cultural and environmental conditions across the life course.

…. at the centre of our approach considering the requirements to improve the health and wellbeing of our area are the wider determinants of health and wellbeing, acknowledging how factors, such as housing, education, the environment and economy, impact on health outcomes and wellbeing over people’s lifetime and are therefore pivotal to our ambition to ameliorate the health of the poorest the quickest. (Paraphrased Document P).

Thirdly, the threats and barriers to health included environmental risks, communicable diseases and associated challenges, non-communicable conditions and diseases, mental health as well as structural barriers. In terms of communicable diseases, COVID-19 predominated. The environmental risks included climate change and air pollution. Non-communicable diseases were considered as a substantial and increasing threat and encompassed a wide range of chronic conditions such as diabetes, and obesity.

‛Long term conditions are the leading causes of death and disability in [case study] and account for most of our health and care spending. Cases of cancer, diabetes, respiratory disease, dementia and cardiovascular disease will increase as the population of [case study] grows and ages.’ (Document A).

Structural barriers to accessing and using support and/or services for health and wellbeing were identified. These barriers included how the services are set up, such as some GP practices asking for proof of a fixed address or form of identification to register. For example:

Complicated systems (such as having to make multiple calls, the need to speak to many people/gatekeepers or to call at specific time) can be a massive barrier to accessing healthcare and appointments. This is the case particularly for people who have complex mental health needs or chaotic/destabilized circumstances. People who do not have stable housing face difficulties in registering for GP and other services that require an address or rely on post to communicate appointments. (Paraphrased Document R).

A structural threat regarding support and/or services for health and wellbeing was the sustainability of current funding with future uncertainty posing potential threats to the delivery of current services. This also affected the ability to adapt and develop the services, or indeed build new ones.

‛I would say the other thing is I have a beef [sic] [disagreement] with pilot studies or new innovations. Often soft funded, temporary funded, charity funded, partnership work run by enthusiasts. Me, I've done them, or supported people doing many of these. And they're great. They can make a huge impact on the individuals involved on that local area. You can see fantastic work. You get inspired and you want to stand up in a crowd and go, “Wahey, isn't this fantastic?” But actually the sad part of it is on these things, I've seen so many where we then see some good, positive work being done, but we can't make it permanent or we can't spread it because there's no funding behind it.’ (LP8).

Facilitators to tackling health inequalities: the assets

The facilitators for improving health and wellbeing and tackling health inequalities are considered as assets which were underpinned by values and principles.

Values driven supported by four key principles

Being values driven was an important concept and considered as the underpinning attitudes or beliefs that guide decision making [ 52 ]. Particularly, the system’s approach was underpinned by a culture and a system's commitment to tackle health inequalities across the documents and interviews. This was also demonstrated by how passionately and emotively some interviewees spoke about their work.

‛There's a really strong desire and ethos around understanding that we will only ever solve these problems as a system, not by individual organisations or even just part of the system working together. And that feels great.’ (LP3).

Other values driving the approach included accountability, justice, and equity. Reducing health inequalities and improving health were considered to be the right things to do. For example:

We feel strongly about social justice and being inclusive, wishing to reflect the diversity of [case study]. We campaign on subjects that are important to people who are older with respect and kindness. (Paraphrased Document O).

Four key principles were identified that crosscut the assets which were:

Shared vision

Strong partnership

Asset-based approaches

Willingness and ability to act on learning

The mandated strategy, identifying priorities for health and wellbeing for the local population with the required actions, provided the shared vision across each part of the system, and provided the foundations for the work. This shared vision was repeated consistently in the documents and interviews from across the system.

[Case study] will be a place where individuals who have the lowest socioeconomic status will ameliorate their health the quickest. [Case study] will be a place for good health and compassion for all people, regardless of their age. (Paraphrased Document A).

‛One thing that is obviously becoming stronger and stronger is the focus on health inequalities within all of that, and making sure that we are helping people and provide support to people with the poorest health as fast as possible, so that agenda hasn’t shifted.’ (LP7).

This drive to embed the reduction of health inequalities was supported by clear new national guidance encapsulated by the NHS Core20PLUS5 priorities. Core20PLUS5 is the UK's approach to support a system to improve their healthcare inequalities [ 53 ]. Additionally, the system's restructuring from Clinical Commissioning Groups (CCGs) to Integrated Care Boards (ICBs) and formalisation of the now statutory Integrated Care Systems (ICS) in England was also reported to facilitate the driving of further improvement in health inequalities. These changes at a regional and local level helped bring key partners across the system (NHS and local government among others) to build upon their collective responsibility for improving health and reducing health inequalities for their area [ 54 ].

‛I don’t remember the last time we’ve had that so clear, or the last time that health inequalities has had such a prominent place, both in the NHS planning guidance or in the NHS contract. ’ (LP15). ‛The Health and Care Act has now got a, kind of, pillar around health inequalities, the new establishment of ICPs and ICBs, and also the planning guidance this year had a very clear element on health inequalities.’ (LP12)

A strong partnership and collaborative team approach across the system underpinned the work from the documents and included the reoccurrence of the concept that this case study acted as one team: ‘Team [case study]'.

Supporting one another to ensure [case study] is the best it can be: Team [case study]. It involves learning, sharing ideas as well as organisations sharing assets and resources, authentic partnerships, and striving for collective impact (environmental and social) to work towards shared goals . (Paraphrased Document B).

This was corroborated in the interviews as working in partnership to tackle health inequalities was considered by the interviewees as moving in the right direction. There were reports that the relationship between local government, health care and the third sector had improved in recent years which was still an ongoing priority:

‘I think the only improvement I would cite, which is not an improvement in terms of health outcomes, but in terms of how we work across [case study] together has moved on quite a lot, in terms of teams leads and talking across us, and how we join up on things, rather than see ourselves all as separate bodies' (LP15).

‘I think the relationship between local authorities and health and the third sector, actually, has much more parity and esteem than it had before.' (LP11)

The approaches described above were supported by all health and care partners signing up to principles around partnership; it is likely this has helped foster the case study's approach. This also builds on the asset-based approaches that were another key principle building on co-production and co-creation which is described below.

We begin with people : instead of doing things to people or for them, we work with them, augmenting the skills, assets and strength of [case study]’s people, workforce and carers. We achieve : actions are focused on over words and by using intelligence, every action hones in on the actual difference that we will make to ameliorate outcomes, quality and spend [case study]’s money wisely; We are Team [case study ]: having kindness, working as one organisation, taking responsibility collectively and delivering on what we agreed. Problems are discussed with a high challenge and high support attitude. (Paraphrased Document D).

At times, the degree to which the asset-based approaches were embedded differed from the documents compared to the interviews, even when from the same part of the system. For example, the documents often referred to the asset-based approach as having occurred whilst interviewees viewed it more as a work in progress.

‘We have re-designed many of our services to focus on needs-led, asset-based early intervention and prevention, and have given citizens more control over decisions that directly affect them .’ (Document M).

‘But we’re trying to take an asset-based approach, which is looking at the good stuff in communities as well. So the buildings, the green space, the services, but then also the social capital stuff that happens under the radar.’ (LP11).

A willingness to learn and put in action plans to address the learning were present. This enables future proofing by building on what is already in place to build the capacity, capability and flexibility of the system. This was particularly important for developing the workforce as described below.

‘So we’ve got a task and finish group set up, […] So this group shows good practice and is a space for people to discuss some of the challenges or to share what interventions they are doing around the table, and also look at what other opportunities that they have within a region or that we could build upon and share and scale.’ (LP12).

These assets that are considered as facilitators are divided into four key levels which are the system, services and support, communities and individuals, and workforce which are discussed in turn below.

Firstly, the system within this case study was made up of many organisations and partnerships within the NHS, local government, VSCE sector and communities. The interviewees reported the presence of a strong VCSE sector which had been facilitated by the local council's commitment to funding this sector:

‘Within [case study], we have a brilliant third sector, the council has been longstanding funders of infrastructure in [case study], third sector infrastructure, to enable those links [of community engagement] to be made' (LP16).

In both the documents and interviews, a strong coherent strategic integrated population health management plan with a system’s approach to embed the reduction of health inequalities was evident. For example, on a system level regionally:

‘To contribute towards a reduction in health inequalities we will: take a system wide approach for improving outcomes for specific groups known to be affected by health inequalities, starting with those living in our most deprived communities….’ (Document H).

This case study’s approach within the system included using creative solutions and harnessing technology. This included making bold and inventive changes to improve how the city and the system linked up and worked together to improve health. For example, regeneration work within the city to ameliorate and transform healthcare facilities as well as certain neighbourhoods by having new green spaces, better transport links in order to improve city-wide innovation and collaboration (paraphrased Document F) were described. The changes were not only related to physical aspects of the city but also aimed at how the city digitally linked up. Being a leader in digital innovation to optimise the health benefits from technology and information was identified in several documents.

‘ Having the best connected city using digital technology to improve health and wellbeing in innovative ways.’ (Document G).

The digital approaches included ongoing development of a digitalised personalised care record facilitating access to the most up-to-date information to developing as well as having the ‘ latest, cutting edge technologies’ ( Document F) in hospital care. However, the importance of not leaving people behind by embedding digital alternatives was recognised in both the documents and interviews.

‘ We are trying to just embed the culture of doing an equity health impact assessment whenever you are bringing in a digital solution or a digital pathway, and that there is always an alternative there for people who don’t have the capability or capacity to use it. ’ (LP1).

The successful one hundred percent [redacted] programme is targeting some of our most digitally excluded citizens in [case study]. For our city to continue to thrive, we all need the appropriate skills, technology and support to get the most out of being online. (Paraphrased Document Q)

This all links in with the system that functions in a ‘place' which includes the importance of where people are born, grow, work and live. Working towards this place being welcoming and appealing was described both regionally and locally. This included aiming to make the case study the place of choice for people.

‘Making [case study] a centre for good growth becoming the place of choice in the UK to live, to study, for businesses to invest in, for people to come and work.’ (Document G).

Services and support

Secondly, a variety of available services and support were described from the local authority, NHS, and voluntary community sectors. Specific areas of work, such as local initiatives (including targeted work or campaigns for specific groups or specific health conditions) as well as parts of the system working together with communities collaboratively, were identified. This included a wide range of work being done such as avoiding delayed discharges or re-admissions, providing high quality affordable housing as well as services offering peer support.

‘We have a community health development programme called [redacted], that works with particular groups in deprived communities and ethnically diverse communities to work in a very trusted and culturally appropriate way on the things that they want to get involved with to support their health.’ (LP3 ).

It is worth noting that reducing health inequalities in avoidable admissions was not often explicitly specified in the documents or interviews. However, either specified or otherwise inferred, preventing ill health and improving access, experience, and outcomes were vital components to addressing inequalities. This was approached by working with communities to deliver services in communities that worked for all people. Having co-designed, accessible, equitable integrated services and support appeared to be key.

‘Reducing inequalities in unplanned admissions for conditions that could be cared for in the community and access to planned hospital care is key.’ (Document H)

Creating plans with people: understanding the needs of local population and designing joined-up services around these needs. (Paraphrased Document A).

‘ So I think a core element is engagement with your population, so that ownership and that co-production, if you're going to make an intervention, don't do it without because you might miss the mark. ’ (LP8).

Clear, consistent and appropriate communication that was trusted was considered important to improve health and wellbeing as well as to tackle health inequalities. For example, trusted community members being engaged to speak on the behalf of the service providers:

‘The messenger is more important than the message, sometimes.’ (LP11).

This included making sure the processes are in place so that the information is accessible for all, including people who have additional communication needs. This was considered as a work in progress in this case study.

‘I think for me, things do come down to those core things, of health, literacy, that digital exclusion and understanding the wider complexities of people.’ (LP12)

‘ But even more confusing if you've got an additional communication need. And we've done quite a lot of work around the accessible information standard which sounds quite dry, and doesn't sound very- but actually, it's fundamental in accessing health and care. And that is, that all health and care organisations should record your communication preferences. So, if I've got a learning disability, people should know. If I've got a hearing impairment, people should know. But the systems don’t record it, so blind people are getting sent letters for appointments, or if I've got hearing loss, the right provisions are not made for appointments. So, actually, we're putting up barriers before people even come in, or can even get access to services.’ (LP16).

Flexible, empowering, holistic care and support that was person-centric was more apparent in the documents than the interviews.

At the centre of our vision is having more people benefiting from the life chances currently enjoyed by the few to make [case study] a more equal place. Therefore, we accentuate the importance of good health, the requirement to boost resilience, and focus on prevention as a way of enabling higher quality service provision that is person-centred. [Paraphrased Document N).

Through this [work], we will give all children and young people in [case study], particularly if they are vulnerable and/or disadvantaged, a start in life that is empowering and enable them to flourish in a compassionate and lively city. [Paraphrased Document M].

Communities and individuals

Thirdly, having communities and individuals at the heart of the work appeared essential and viewed as crucial to nurture in this case study. The interconnectedness of the place, communities and individuals were considered a key part of the foundations for good health and wellbeing.

In [case study], our belief is that our people are our greatest strength and our most important asset. Wellbeing starts with people: our connections with our friends, family, and colleagues, our behaviour, understanding, and support for one another, as well as the environment we build to live in together . (Paraphrased Document A).

A recognition of the power of communities and individuals with the requirement to support that key principle of a strength-based approach was found. This involved close working with communities to help identify what was important, what was needed and what interventions would work. This could then lead to improved resilience and cohesion.

‛You can't make effective health and care decisions without having the voice of people at the centre of that. It just won't work. You won't make the right decisions.’ (LP16).

‘Build on the strengths in ourselves, our families, carers and our community; working with people, actively listening to what matters most to people, with a focus on what’s strong rather than what’s wrong’ (Document G).

Meaningful engagement with communities as well as strengths and asset-based approaches to ensure self-sufficiency and sustainability of communities can help communities flourish. This includes promoting friendships, building community resilience and capacity, and inspiring residents to find solutions to change the things they feel needs altering in their community . (Paraphrased Document B).

This close community engagement had been reported to foster trust and to lead to improvements in health.

‘But where a system or an area has done a lot of community engagement, worked really closely with the community, gained their trust and built a programme around them rather than just said, “Here it is. You need to come and use it now,” you can tell that has had the impact. ' (LP1).

Finally, workforce was another key asset; the documents raised the concept of one workforce across health and care. The key principles of having a shared vision, asset-based approaches and strong partnership were also present in this example:

By working together, the Health and Care sector makes [case study] the best area to not only work but also train for people of all ages. Opportunities for skills and jobs are provided with recruitment and engagement from our most disadvantaged communities, galvanizing the future’s health and care workforce. By doing this, we have a very skilled and diverse workforce we need to work with our people now as well as in the future. (Paraphrased Document E).

An action identified for the health and care system to address health inequalities in case study 1 was ‘ the importance of having an inclusive workforce trained in person-centred working practices ’ (Document R). Several ways were found to improve and support workforce skills development and embed awareness of health inequalities in practice and training. Various initiatives were available such as an interactive health inequalities toolkit, theme-related fellowships, platforms and networks to share learning and develop skills.

‛We've recently launched a [redacted] Fellowship across [case study’s region], and we've got a number of clinicians and managers on that………. We've got training modules that we've put on across [case study’s region], as well for health inequalities…we've got learning and web resources where we share good practice from across the system, so that is our [redacted] Academy.’ (LP2).

This case study also recognised the importance of considering the welfare of the workforce; being skilled was not enough. This had been recognised pre-pandemic but was seen as even more important post COVID-19 due to the impact that COVID-19 had on staff, particularly in health and social care.

‛The impacts of the pandemic cannot be underestimated; our colleagues and services are fatigued and still dealing with the pressures. This context makes it even more essential that we share the responsibility, learn from each other at least and collaborate with each other at best, and hold each other up to be the best we can.’ (Document U).

Concerns were raised such as the widening of health inequalities since the pandemic and cost of living crisis. Post-pandemic and Brexit, recruiting health, social care and third sector staff was compounding the capacity throughout this already heavily pressurised system.

In [case study], we have seen the stalling of life expectancy and worsening of the health inequality gap, which is expected to be compounded by the effects of the pandemic. (Paraphrased Document T)

‘I think key barriers, just the immense pressure on the system still really […] under a significant workload, catching up on activity, catching up on NHS Health Checks, catching up on long-term condition reviews. There is a significant strain on the system still in terms of catching up. It has been really difficult because of the impact of COVID.’ (LP7).

‘Workforce is a challenge, because the pipelines that we’ve got, we’ve got fewer people coming through many of them. And that’s not just particular to, I don't know, nursing, which is often talking talked [sic] about as a challenged area, isn't it? And of course, it is. But we’ve got similar challenges in social care, in third sector.’ (LP5).

The pandemic was reported to have increased pressures on the NHS and services not only in relation to staff capacity but also regarding increases in referrals to services, such as mental health. Access to healthcare changed during the pandemic increasing barriers for some:

‘I think people are just confused about where they're supposed to go, in terms of accessing health and care at the moment. It's really complex to understand where you're supposed to go, especially, at the moment, coming out of COVID, and the fact that GPs are not the accessible front door. You can't just walk into your GP anymore.’ (LP16).

‘Meeting this increased demand [for work related to reducing ethnic inequalities in mental health] is starting to prove a challenge and necessitates some discussion about future resourcing.’ (Document S)

Several ways were identified to aid effective adaptation and/or mitigation. This included building resilience such as developing the existing capacity, capability and flexibility of the system by learning from previous work, adapting structures and strengthening workforce development. Considerations, such as a commitment to Marmot Principles and how funding could/would contribute, were also discussed.

The funding’s [linked to Core20PLUS5] purpose is to help systems to ensure that health inequalities are not made worse when cost-savings or efficiencies are sought…The available data and insight are clear and [health inequalities are] likely to worsen in the short term, the delays generated by pandemic, the disproportionate effect of that on the most deprived and the worsening food and fuel poverty in all our places. (Paraphrased Document L).

Learning from the pandemic was thought to be useful as some working practices had altered during COVID-19 for the better, such as needing to continue to embed how the system had collaborated and resist old patterns of working:

‘So I think that emphasis between collaboration – extreme collaboration – which is what we did during COVID is great. I suppose the problem is, as we go back into trying to save money, we go back into our old ways of working, about working in silos. And I think we’ve got to be very mindful of that, and continue to work in a different way.’ (LP11).

Another area identified as requiring action, was the collection, analysis, sharing and use of data accessible by the whole system.

‘So I think there is a lot of data out there. It’s just how do we present that in such a way that it’s accessible to everyone as well, because I think sometimes, what happens is that we have one group looking at data in one format, but then how do we cascade that out?’ (LP12)

We aimed to explore a system’s level understanding of how a local area addresses health inequalities with a focus on avoidable emergency admissions using a case study approach. Therefore, the focus of our research was strategic and systematic approaches to inequalities reduction. Gaining an overview of what was occurring within a system is pertinent because local areas are required to have a regard to address health inequalities in their local areas [ 20 , 21 ]. Through this exploration, we also developed an understanding of the system's processes reported to be required. For example, an area requiring action was viewed as the accessibility and analysis of data. The case study described having health inequalities ‘at the heart of its health and wellbeing strategy ’ which was echoed across the documents from multiple sectors across the system. Evidence of a values driven partnership with whole systems working was centred on the importance of place and involving people, with links to a ‘strong third sector ’ . Working together to support and strengthen local assets (the system, services/support, communities/individuals, and the workforce) were vital components. This suggested a system’s committed and integrated approach to improve population health and reduce health inequalities as well as concerted effort to increase system resilience. However, there was juxtaposition at times with what the documents contained versus what interviewees spoke about, for example, the degree to which asset-based approaches were embedded.

Furthermore, despite having a priori codes for the documentary analysis and including specific questions around work being undertaken to reduce health inequalities in avoidable admissions in the interviews with key systems leaders, this explicit link was still very much under-developed for this case study. For example, how to reduce health inequalities in avoidable emergency admissions was not often specified in the documents but could be inferred from existing work. This included work around improving COVID-19 vaccine uptake in groups who were identified as being at high-risk (such as older people and socially excluded populations) by using local intelligence to inform where to offer local outreach targeted pop-up clinics. This limited explicit action linking reduction of health inequalities in avoidable emergency admissions was echoed in the interviews and it became clear as we progressed through the research that a focus on reduction of health inequalities in avoidable hospital admissions at a systems level was not a dominant aspect of people’s work. Health inequalities were viewed as a key part of the work but not necessarily examined together with avoidable admissions. A strengthened will to take action is reported, particularly around reducing health inequalities, but there were limited examples of action to explicitly reduce health inequalities in avoidable admissions. This gap in the systems thinking is important to highlight. When it was explicitly linked, upstream strategies and thinking were acknowledged as requirements to reduce health inequalities in avoidable emergency admissions.

Similar to our findings, other research have also found networks to be considered as the system’s backbone [ 30 ] as well as the recognition that communities need to be central to public health approaches [ 51 , 55 , 56 ]. Furthermore, this study highlighted the importance of understanding the local context by using local routine and bespoke intelligence. It demonstrated that population-based approaches to reduce health inequalities are complex, multi-dimensional and interconnected. It is not about one part of the system but how the whole system interlinks. The interconnectedness and interdependence of the system (and the relevant players/stakeholders) have been reported by other research [ 30 , 57 ], for example without effective exchange of knowledge and information, social networks and systems do not function optimally [ 30 ]. Previous research found that for systems to work effectively, management and transfer of knowledge needs to be collaborative [ 30 ], which was recognised in this case study as requiring action. By understanding the context, including the strengths and challenges, the support or action needed to overcome the barriers can be identified.

There are very limited number of case studies that explore health inequalities with a focus on hospital admissions. Of the existing research, only one part of the health system was considered with interviews looking at data trends [ 35 ]. To our knowledge, this research is the first to build on this evidence by encompassing the wider health system using wider-ranging interviews and documentary analysis. Ford et al. [ 35 ] found that geographical areas typically had plans to reduce total avoidable emergency admissions but not comprehensive plans to reduce health inequalities in avoidable emergency admissions. This approach may indeed widen health inequalities. Health inequalities have considerable health and costs impacts. Pertinently, the hospital care costs associated with socioeconomic inequalities being reported as £4.8 billion a year, mainly due to excess hospitalisations such as avoidable admissions [ 58 ] and the burden of disease lies disproportionately with our most disadvantaged communities, addressing inequalities in hospital pressures is required [ 25 , 26 ].

Implications for research and policy

Improvements to life expectancy have stalled in the UK with a widening of health inequalities [ 12 ]. Health inequalities are not inevitable; it is imperative that the health gap between the deprived and affluent areas is narrowed [ 12 ]. This research demonstrates the complexity and intertwining factors that are perceived to address health inequalities in an area. Despite the evidence of the cost (societal and individual) of avoidable admissions, explicit tackling of inequality in avoidable emergency admissions is not yet embedded into the system, therefore highlights an area for policy and action. This in-depth account and exploration of the characteristics of ‘whole systems’ working to address health inequalities, including where challenges remain, generated in this research will be instrumental for decision makers tasked with addressing health and care inequalities.

This research informs the next step of exploring each identified theme in more detail and moving beyond description to develop tools, using a suite of multidimensional and multidisciplinary methods, to investigate the effects of interventions on systems as previously highlighted by Rutter et al. [ 5 ].

Strengths and limitations

Documentary analysis is often used in health policy research but poorly described [ 44 ]. Furthermore, Yin reports that case study research is often criticised for not adhering to ‘systematic procedures’ p. 18 [ 41 ]. A clear strength of this study was the clearly defined boundary (in time and space) case as well as following a defined systematic approach, with critical thought and rationale provided at each stage [ 34 , 41 ]. A wide range and large number of documents were included as well as interviewees from across the system thereby resulting in a comprehensive case study. Integrating the analysis from two separate methodologies (interviews and documentary analysis), analysed separately before being combined, is also a strength to provide a coherent rich account [ 49 ]. We did not limit the reasons for hospital admission to enable a broad as possible perspective; this is likely to be a strength in this case study as this connection between health inequalities and avoidable hospital admissions was still infrequently made. However, for example, if a specific care pathway for a health condition had been highlighted by key informants this would have been explored.

Due to concerns about identifiability, we took several steps. These included providing a summary of the sectors that the interviewees and document were from but we were not able to specify which sectors each quote pertained. Additionally, some of the document quotes required paraphrasing. However, we followed a set process to ensure this was as rigorous as possible as described in the methods section. For example, where we were required to paraphrase, each paraphrased quote and original was shared and agreed with all the authors to reduce the likelihood to inadvertently misinterpreting or misquoting.

The themes are unlikely to represent an exhaustive list of the key elements requiring attention, but they represent the key themes that were identified using a robust methodological process. The results are from a single urban local authority with high levels of socioeconomic disadvantage in the North of England which may limit generalisability to different contexts. However, the findings are still generalisable to theoretical considerations [ 41 ]. Attempts to integrate a case study with a known framework can result in ‘force-fit’ [ 34 ] which we avoided by developing our own framework (Fig. 1 ) considering other existing models [ 14 , 59 ]. The results are unable to establish causation, strength of association, or direction of influence [ 60 ] and disentangling conclusively what works versus what is thought to work is difficult. The documents’ contents may not represent exactly what occurs in reality, the degree to which plans are implemented or why variation may occur or how variation may affect what is found [ 43 , 61 ]. Further research, such as participatory or non-participatory observation, could address this gap.

Conclusions

This case study provides an in-depth exploration of how local areas are working to address health and care inequalities, with a focus on avoidable hospital admissions. Key elements of this system’s reported approach included fostering strategic coherence, cross-agency working, and community-asset based working. An area requiring action was viewed as the accessibility and analysis of data. Therefore, local areas could consider the challenges of data sharing across organisations as well as the organisational capacity and capability required to generate useful analysis in order to create meaningful insights to assist work to reduce health and care inequalities. This would lead to improved understanding of the context including where the key barriers lie for a local area. Addressing structural barriers and threats as well as supporting the training and wellbeing of the workforce are viewed as key to building resilience within a system to reduce health inequalities. Furthermore, more action is required to embed reducing health inequalities in avoidable admissions explicitly in local areas with inaction risking widening the health gap.

Availability of data and materials

Individual participants’ data that underlie the results reported in this article and a data dictionary defining each field in the set are available to investigators whose proposed use of the data has been approved by an independent review committee for work. Proposals should be directed to [email protected] to gain access, data requestors will need to sign a data access agreement. Such requests are decided on a case by case basis.

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Acknowledgements

Thanks to our Understanding Factors that explain Avoidable hospital admission Inequalities - Research study (UNFAIR) PPI contributors, for their involvement in the project particularly in the identification of the key criteria for the sampling frame. Thanks to the research advisory team as well.

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This research was funded by the National Institute for Health and Care Research (NIHR), grant number (ref CA-CL-2018-04-ST2-010). The funding body was not involved in the study design, collection of data, inter-pretation, write-up, or submission for publication. The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care or Newcastle University.

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Conceptualization - J.L. and S.S.; methodology - C.P.-C., J.L. & S.S.; formal analysis - C. P.-C. & L.M.; investigation- C. P.-C. & L.M., resources, writing of draft manuscript - C.P.-C.; review and editing manuscript L.M., J.L., & S.S.; visualization including figures and tables - C.P.-C.; supervision - J.L. & S.S.; project administration - L.M. & S.S.; funding acquisition - S.S. All authors have read and agreed to the published version of the manuscript.

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Parbery-Clark, C., McSweeney, L., Lally, J. et al. How can health systems approach reducing health inequalities? An in-depth qualitative case study in the UK. BMC Public Health 24 , 2168 (2024). https://doi.org/10.1186/s12889-024-19531-5

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What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. 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. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

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What is Qualitative in Research

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If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research 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)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” 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. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

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Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

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Patrik Aspers

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Aspers, P., Corte, U. What is Qualitative in Qualitative Research. Qual Sociol 42 , 139–160 (2019). https://doi.org/10.1007/s11133-019-9413-7

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Published : 27 February 2019

Issue Date : 01 June 2019

DOI : https://doi.org/10.1007/s11133-019-9413-7

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