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Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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The Oxford Handbook of Qualitative Research (2nd edn)

Patricia Leavy Independent Scholar Kennebunk, ME, USA

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The Oxford Handbook of Qualitative Research, second edition, presents a comprehensive retrospective and prospective review of the field of qualitative research. Original, accessible chapters written by interdisciplinary leaders in the field make this a critical reference work. Filled with robust examples from real-world research; ample discussion of the historical, theoretical, and methodological foundations of the field; and coverage of key issues including data collection, interpretation, representation, assessment, and teaching, this handbook aims to be a valuable text for students, professors, and researchers. This newly revised and expanded edition features up-to-date examples and topics, including seven new chapters on duoethnography, team research, writing ethnographically, creative approaches to writing, writing for performance, writing for the public, and teaching qualitative research.

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Your ultimate guide to qualitative research (with methods and examples).

16 min read You may be already using qualitative research and want to check your understanding, or you may be starting from the beginning. Learn about qualitative research methods and how you can best use them for maximum effect.

What is qualitative research?

Qualitative research is a research method that collects non-numerical data. Typically, it goes beyond the information that quantitative research provides (which we will cover below) because it is used to gain an understanding of underlying reasons, opinions, and motivations.

Qualitative research methods focus on the thoughts, feelings, reasons, motivations, and values of a participant, to understand why people act in the way they do .

In this way, qualitative research can be described as naturalistic research, looking at naturally-occurring social events within natural settings. So, qualitative researchers would describe their part in social research as the ‘vehicle’ for collecting the qualitative research data.

Qualitative researchers discovered this by looking at primary and secondary sources where data is represented in non-numerical form. This can include collecting qualitative research data types like quotes, symbols, images, and written testimonials.

These data types tell qualitative researchers subjective information. While these aren’t facts in themselves, conclusions can be interpreted out of qualitative that can help to provide valuable context.

Because of this, qualitative research is typically viewed as explanatory in nature and is often used in social research, as this gives a window into the behavior and actions of people.

It can be a good research approach for health services research or clinical research projects.

Free eBook: The qualitative research design handbook

Quantitative vs qualitative research

In order to compare qualitative and quantitative research methods, let’s explore what quantitative research is first, before exploring how it differs from qualitative research.

Quantitative research

Quantitative research is the research method of collecting quantitative research data – data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed .

Quantitative research methods deal with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data.

Quantitative research data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

The difference between quantitative and qualitative research methodology

While qualitative research is defined as data that supplies non-numerical information, quantitative research focuses on numerical data.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative research methods. If you want to explore ideas, thoughts, and meanings, use qualitative research methods.

While qualitative research helps you to properly define, promote and sell your products, don’t rely on qualitative research methods alone because qualitative findings can’t always be reliably repeated. Qualitative research is directional, not empirical.

The best statistical analysis research uses a combination of empirical data and human experience ( quantitative research and qualitative research ) to tell the story and gain better and deeper insights, quickly.

Where both qualitative and quantitative methods are not used, qualitative researchers will find that using one without the other leaves you with missing answers.

For example, if a retail company wants to understand whether a new product line of shoes will perform well in the target market:

• Qualitative research methods could be used with a sample of target customers, which would provide subjective reasons why they’d be likely to purchase or not purchase the shoes, while
• Quantitative research methods into the historical customer sales information on shoe-related products would provide insights into the sales performance, and likely future performance of the new product range.

Approaches to qualitative research

There are five approaches to qualitative research methods:

• Grounded theory: Grounded theory relates to where qualitative researchers come to a stronger hypothesis through induction, all throughout the process of collecting qualitative research data and forming connections. After an initial question to get started, qualitative researchers delve into information that is grouped into ideas or codes, which grow and develop into larger categories, as the qualitative research goes on. At the end of the qualitative research, the researcher may have a completely different hypothesis, based on evidence and inquiry, as well as the initial question.
• Ethnographic research : Ethnographic research is where researchers embed themselves into the environment of the participant or group in order to understand the culture and context of activities and behavior. This is dependent on the involvement of the researcher, and can be subject to researcher interpretation bias and participant observer bias . However, it remains a great way to allow researchers to experience a different ‘world’.
• Action research: With the action research process, both researchers and participants work together to make a change. This can be through taking action, researching and reflecting on the outcomes. Through collaboration, the collective comes to a result, though the way both groups interact and how they affect each other gives insights into their critical thinking skills.
• Phenomenological research: Researchers seek to understand the meaning of an event or behavior phenomenon by describing and interpreting participant’s life experiences. This qualitative research process understands that people create their own structured reality (‘the social construction of reality’), based on their past experiences. So, by viewing the way people intentionally live their lives, we’re able to see the experiential meaning behind why they live as they do.
• Narrative research: Narrative research, or narrative inquiry, is where researchers examine the way stories are told by participants, and how they explain their experiences, as a way of explaining the meaning behind their life choices and events. This qualitative research can arise from using journals, conversational stories, autobiographies or letters, as a few narrative research examples. The narrative is subjective to the participant, so we’re able to understand their views from what they’ve documented/spoken.

Qualitative research methods can use structured research instruments for data collection, like:

Surveys for individual views

A survey is a simple-to-create and easy-to-distribute qualitative research method, which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Qualitative research questions tend to be open questions that ask for more information and provide a text box to allow for unconstrained comments.

Examples include:

• Asking participants to keep a written or a video diary for a period of time to document their feelings and thoughts
• In-Home-Usage tests: Buyers use your product for a period of time and report their experience

Surveys for group consensus (Delphi survey)

A Delphi survey may be used as a way to bring together participants and gain a consensus view over several rounds of questions. It differs from traditional surveys where results go to the researcher only. Instead, results go to participants as well, so they can reflect and consider all responses before another round of questions are submitted.

This can be useful to do as it can help researchers see what variance is among the group of participants and see the process of how consensus was reached.

• Asking participants to act as a fake jury for a trial and revealing parts of the case over several rounds to see how opinions change. At the end, the fake jury must make a unanimous decision about the defendant on trial.
• Asking participants to comment on the versions of a product being developed , as the changes are made and their feedback is taken onboard. At the end, participants must decide whether the product is ready to launch .

Semi-structured interviews

Interviews are a great way to connect with participants, though they require time from the research team to set up and conduct, especially if they’re done face-to-face.

Researchers may also have issues connecting with participants in different geographical regions. The researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

• Conducting a phone interview with participants to run through their feedback on a product . During the conversation, researchers can go ‘off-script’ and ask more probing questions for clarification or build on the insights.

Focus groups

Participants are brought together into a group, where a particular topic is discussed. It is researcher-led and usually occurs in-person in a mutually accessible location, to allow for easy communication between participants in focus groups.

In focus groups , the researcher uses a set of predefined open-ended questions, though more ad-hoc questions can be asked depending on participant answers.

• Asking participants to do UX tests, which are interface usability tests to show how easily users can complete certain tasks

Direct observation

This is a form of ethnographic research where researchers will observe participants’ behavior in a naturalistic environment. This can be great for understanding the actions in the culture and context of a participant’s setting.

This qualitative research method is prone to researcher bias as it is the researcher that must interpret the actions and reactions of participants. Their findings can be impacted by their own beliefs, values, and inferences.

• Embedding yourself in the location of your buyers to understand how a product would perform against the values and norms of that society

Qualitative data types and category types

Qualitative research methods often deliver information in the following qualitative research data types:

• Written testimonials

Through contextual analysis of the information, researchers can assign participants to category types:

• Social class
• Political alignment
• Most likely to purchase a product
• Their preferred training learning style

Advantages of qualitative research

• Useful for complex situations: Qualitative research on its own is great when dealing with complex issues, however, providing background context using quantitative facts can give a richer and wider understanding of a topic. In these cases, quantitative research may not be enough.
• A window into the ‘why’: Qualitative research can give you a window into the deeper meaning behind a participant’s answer. It can help you uncover the larger ‘why’ that can’t always be seen by analyzing numerical data.
• Can help improve customer experiences: In service industries where customers are crucial, like in private health services, gaining information about a customer’s experience through health research studies can indicate areas where services can be improved.

Disadvantages of qualitative research

• You need to ask the right question: Doing qualitative research may require you to consider what the right question is to uncover the underlying thinking behind a behavior. This may need probing questions to go further, which may suit a focus group or face-to-face interview setting better.
• Results are interpreted: As qualitative research data is written, spoken, and often nuanced, interpreting the data results can be difficult as they come in non-numerical formats. This might make it harder to know if you can accept or reject your hypothesis.
• More bias: There are lower levels of control to qualitative research methods, as they can be subject to biases like confirmation bias, researcher bias, and observation bias. This can have a knock-on effect on the validity and truthfulness of the qualitative research data results.

How to use qualitative research to your business’s advantage?

Qualitative methods help improve your products and marketing in many different ways:

• Understand the emotional connections to your brand
• Identify obstacles to purchase
• Uncover doubts and confusion about your messaging
• Find missing product features
• Improve the usability of your website, app, or chatbot experience
• Learn about how consumers talk about your product
• See how buyers compare your brand to others in the competitive set
• Learn how an organization’s employees evaluate and select vendors

6 steps to conducting good qualitative research

Businesses can benefit from qualitative research by using it to understand the meaning behind data types. There are several steps to this:

• Define your problem or interest area: What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis: Ask yourself what could be the causes for the situation with those qualitative research data types.
• Plan your qualitative research: Use structured qualitative research instruments like surveys, focus groups, or interviews to ask questions that test your hypothesis.
• Data Collection: Collect qualitative research data and understand what your data types are telling you. Once data is collected on different types over long time periods, you can analyze it and give insights into changing attitudes and language patterns.
• Data analysis: Does your information support your hypothesis? (You may need to redo the qualitative research with other variables to see if the results improve)
• Effectively present the qualitative research data: Communicate the results in a clear and concise way to help other people understand the findings.

Qualitative data analysis

Evaluating qualitative research can be tough when there are several analytics platforms to manage and lots of subjective data sources to compare.

Qualtrics provides a number of qualitative research analysis tools, like Text iQ , powered by Qualtrics iQ, provides powerful machine learning and native language processing to help you discover patterns and trends in text.

This also provides you with:

• Sentiment analysis — a technique to help identify the underlying sentiment (say positive, neutral, and/or negative) in qualitative research text responses
• Topic detection/categorisation — this technique is the grouping or bucketing of similar themes that can are relevant for the business & the industry (eg. ‘Food quality’, ‘Staff efficiency’ or ‘Product availability’)

How Qualtrics products can enhance & simplify the qualitative research process

Even in today’s data-obsessed marketplace, qualitative data is valuable – maybe even more so because it helps you establish an authentic human connection to your customers. If qualitative research doesn’t play a role to inform your product and marketing strategy, your decisions aren’t as effective as they could be.

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting qualitative research. From survey creation and data collection to textual analysis and data reporting, it can help all your internal teams gain insights from your subjective and categorical data.

Qualitative methods are catered through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of qualitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Text IQ™ and Driver IQ™ make analyzing subjective and categorical data easy and simple. Choose to highlight key findings based on topic, sentiment, or frequency. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

• 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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Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

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Criterion Validity: Definition & Examples

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Home Market Research

Qualitative Research Methods: Types, Analysis + Examples

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

What is qualitative research?

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

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

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

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

Gather research insights

Types of qualitative research methods with examples

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

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

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

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

1. One-on-one interview

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

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

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

2. Focus groups

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

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

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

3. Ethnographic research

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

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

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

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

4. Case study research

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

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

5. Record keeping

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

6. Process of observation

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

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

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative research: data collection and analysis

A. qualitative data collection.

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

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

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

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

B. Qualitative data analysis

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

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

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

Characteristics of qualitative research methods

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

Qualitative research method case study

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

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

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

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

When to use qualitative research

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

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

LEARN ABOUT: Steps in Qualitative Research

Qualitative research methods vs quantitative research methods

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

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

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Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Copyright © 2024, StatPearls Publishing LLC.

• Introduction
• Issues of Concern
• Clinical Significance
• Enhancing Healthcare Team Outcomes
• Review Questions

Publication types

• Study Guide

An Overview of Qualitative Research Methods

Direct Observation, Interviews, Participation, Immersion, Focus Groups

• Research, Samples, and Statistics
• Key Concepts
• Major Sociologists
• News & Issues
• Recommended Reading
• Archaeology

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.

• How to Conduct a Sociology Research Interview
• Definition of Idiographic and Nomothetic
• What Is Participant Observation Research?
• Conducting Case Study Research in Sociology
• Pilot Study in Research
• How to Understand Interpretive Sociology
• What Is Ethnography?
• Understanding Secondary Data and How to Use It in Research
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• Research in Essays and Reports
• Definition and Overview of Grounded Theory
• What Is Naturalistic Observation? Definition and Examples
• Content Analysis: Method to Analyze Social Life Through Words, Images
• Macro- and Microsociology
• A Review of Software Tools for Quantitative Data Analysis
• The Sociology of the Internet and Digital Sociology

• 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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DOI : https://doi.org/10.1186/s42466-020-00059-z

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

Qualitative vs. Quantitative: Key Differences in Research Types

L et's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data.

With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics.

Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them.

What Is Qualitative Research?

Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis.

In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions.

This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved.

Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data.

Examples of How to Use Qualitative Research

The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research.

Here are just a few ways you can use qualitative research.

• To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values.
• To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments.
• To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns.

Types of Qualitative Data Collection

Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including:

• Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes.
• Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions.
• Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic.
• Visual data : You can learn new information through images, photographs, videos or other visual materials.

What Is Quantitative Research?

Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis.

Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions.

Examples of How to Use Quantitative Research

Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods.

• Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period.
• Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood.

Types of Quantitative Data

Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use.

• Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina.
• Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins.
• Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry.

Qualitative vs. Quantitative Research: 3 Key Differences

Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ.

• Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis.
• Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data.
• Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences.

Benefits of Combining Qualitative and Quantitative Research

You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including:

• A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.
• Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results.
• Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study.

This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

Original article: Qualitative vs. Quantitative: Key Differences in Research Types

11 Types of qualitative research marketers navigate every day

Types of qualitative research methods, when to conduct qualitative research, get the best of both worlds with attest market research platform.

Is your marketing or product development a bit weak and under the weather, or isn’t it as punchy as it used to be? Qualitative research might just be the pick-me-up it needs. Now, not just any type of qualitative market research (it’s not some magic cure-all). You need to pick the right type of qualitative research — and we’re here to help you do that.

But what you need to know about qualitative research at its core, is that it’s about exploring the qualities and nuances of human behavior and preferences. Using discussions, observations, and analysis, you try to uncover not just what people do, but why they do it.

Conducting qualitative research provides you with rich, detailed feedback that gives depth to – and compliments – quantitative research, and can help you formulate direct actions to take. Here’s which qualitative methods we’ll be exploring today.

• Focus groups
• Observation
• Content analysis 
• Narrative analysis
• Historical records management and case studies
• Ethnographic research
• Phenomenological research
• Grounded theory method
• Action research

1. Qualitative research surveys

Surveys are great for tapping into the minds of your audience: you can ask direct questions to gather feedback on everything, in a variety of formats.

With the flexibility to reach a broad audience and the ability to tailor your questions for specific insights, surveys are one of the most used tools for gathering qualitative data at scale, and in record speed.

• Collect feedback from a wide range of participants quickly.
• Tailor surveys to explore various aspects of consumer behavior, from product preferences to brand perception.
• Compared to other qualitative methods, surveys are relatively low-cost and can be distributed widely with minimal resources.

Challenges and solutions:

• Formulating questions that get deep, meaningful responses can be tricky. Focus on open-ended questions and avoid leading or biased phrasing.
• Keeping respondents interested and encouraging thoughtful responses is tricky. Offer incentives and ensure the survey is quick and clear to boost engagement and completion rates.
• The pile of qualitative data from open-ended survey responses can be a lot to work through, xo make sure you’re prepped for your qualitative data analysis.

When to use:

Use surveys to explore consumer sentiments, identify unmet needs and pain points, and evaluate what drives brand loyalty.

Send out survey questions and collect written answers or even video responses with Attest . Our platform takes care of everything, from survey templates to get you started, to best-in-class research advice to help you run truly great research.

See how qual research with Attest works

You can get high-quality video responses from your target audiences with Attest, and our team of research pros is on hand to help you run awesome research

2. Interviews

If you want to go deep, and not necessarily get a lot of data from different participants, interviews are your thing. By sitting down for a one-on-one with people from your target audience you can gather detailed feedback and personal stories

• You can follow the conversation wherever it leads, asking follow-up questions that bring out detailed or surprising insights.
• Human-to-human interactions can lead to more genuine responses, giving you a clearer picture of your audience.
• Interviews take a lot of time to conduct and analyze. Using transcription software and focusing your questions can speed things up.
• People might tell you what they think you want to hear. Make sure you create a comfortable setting and assure anonymity to encourage brutal honesty and fight bias.
• Data from interviews can be hard to compare. Sticking to a set of core questions while allowing for (controlled) personal exploration can help.

Use Interviews for qualitative research when developing new products or features to deeply understand user needs and reactions, and for branding or campaigns to gather stories and emotions that tie people to your brand, enriching your next marketing initiative.

3. Focus groups

Learn to read the room. Focus groups bring together a small group of people from your target market to discuss their opinions and experiences regarding your product or service. The setup of these groups often encourages participants to share their thoughts and ideas.

• Bringing together a variety of viewpoints and hearing how they compare to each other helps you understand the nuances of your target audience.
• Group discussions can lead to surprising angles and new insights into consumer attitudes and perceptions that individual interviews may not capture.
• Participants might sway towards consensus opinions. Encouraging open dialogue and using a skilled moderator can help avoid this. And make sure your group is diverse enough as well.
• Individuals can be overlooked in group settings. Feel like some voices are overpowering? Complement focus groups with one-on-one interviews for deeper insights.
• Organizing focus groups is pretty resource-intensive. Virtual focus groups or streamlined in-person sessions are more flexible.

Use focus groups for brand perception studies to delve into group discussions about your brand and for concept testing to gather immediate reactions to new product ideas, packaging, or marketing strategies.

4. Observation

Watching how people interact with your product or service in their natural environment (in person or through video recordings), without interference, is a great way to get real-life insights into user behavior, preferences, and potential improvements that might not be revealed through direct questioning.

• Beat assumptions and get a contextual understanding of how people interact with your product or service in real-world settings.
• Body language and other non-verbal signals can tell you a lot about how consumers feel when handling your product.
• the presence of an observer might make people change their behavior. Unobtrusive methods like video recording can help avoid that.
• Observers might interpret actions through their own bias. Make sure they are well-trained to avoid this, and that you work with multiple observers to compare interpretations.
• Translating observations into actionable data can be challenging. Structured observation guides and analytical frameworks can streamline your analysis.

Use Observation for user experience research to see how people interact with your product in real settings and for environmental impact studies to understand how different environments influence consumer behavior towards your brand.

5. Content analysis 

The words, images or videos related to your brand or product that people create and share tell a story. With content analysis, you collect all these elements and try to find themes, patterns or issues that stand out.

• You don’t have to worry about getting brand-new data in, which also makes it a more cost-effective and sometimes faster qualitative research method.
• With social listening and content analysis, you can identify emerging trends early in. All you need to do is really zoom in.
• The amount of available content is probably going to be overwhelming, but there are plenty of software tools for sentiment analysis out there that do the heavy lifting for you.
• Unhappy customers might be louder than the happy ones, so the content might not represent the broader audience. Balance your content analysis with direct research methods like surveys or interviews to mitigate this bias.

Use content analysis for uncovering insights into brand perception and evaluating the impact of marketing campaigns on public sentiment through social media content analysis.

6. Narrative analysis

Narrative analysis delves into the stories people tell about their experiences with your product or service. It focuses on understanding the sequence of events, the context, and the emotional journeys described by consumers.

• Unpacks the emotional journey and personal experiences of consumers, offering a rich understanding of their relationship with your product or service.
• By analyzing stories, you capture not just the facts but the context around consumer decisions and experiences, revealing deeper motivations.
• Stories often reflect broader cultural and social influences, helping you see how these factors impact consumer behavior.
• Personal biases can influence how narratives are interpreted. Establishing a clear analytical framework and involving multiple analysts can reduce bias.
• Narrative analysis can be detail-oriented and time-consuming. Using software to assist in data coding and thematic analysis can streamline the process.
• It can be challenging to ensure that the narratives collected are directly relevant to your research questions. Carefully designing the prompt and selection criteria for participants can help focus the stories gathered.

Use narrative analysis to map out detailed consumer journeys from first awareness to loyalty and to craft compelling brand stories that resonate deeply with your audience.

7. Historical records management and case studies

This method involves analyzing existing documents and records related to your market or industry, and conducting case studies on specific examples within your field. You look at historical trends, previous campaigns, product launches, and customer feedback over time, providing a context for current market dynamics and guiding future strategies.

• Offers a perspective on how consumer behaviors and market trends have evolved, giving you context for current data.
• You can measure the impact of changes or interventions tend to make in your marketing strategy or product development.
• Historical records may be scattered or difficult to access, so digitize records and maintain a centralized database now for future researchers.
• Ensuring that historical data is still relevant to current contexts can be challenging, so regularly update your data collection and analysis methods to reflect current market conditions.

Use historical records management and case studies for analyzing long-term market trends, assessing the effectiveness of marketing campaigns over time, and understanding the evolution of product life cycles influenced by consumer preferences.

8. Ethnographic research

Ethnographic research immerses you in the everyday lives of your target audience, observing them in their natural settings to understand their behaviors, rituals, and the social context of product usage. This gives you culturally grounded insights into how and why your product fits into consumers’ lives.

• By observing people in their natural environments, you get to see how they genuinely interact with products or services, unfiltered by self-reporting biases.
• You get detailed descriptions of people’s lives and interactions, and much more nuanced insights than numbers and charts.
• You’ll need significant time in the field and enough resources to do it right. Streamlining focus areas and using digital tools for data collection can help manage the workload.
• Immersion in a community or culture can lead to biased perspectives. Regular reflection sessions and involving multiple researchers can help maintain a balanced viewpoint.

Use ethnographic research to understand how user environments and cultures affect product use, tailor offerings for specific markets or cultural groups, and innovate with designs centered on real-world user behavior.

9. Phenomenological research

Phenomenological research focuses on the lived experiences of individuals regarding a particular phenomenon. Through in-depth interviews and discussions, you gather detailed personal accounts, looking for the underlying meanings and emotions attached to experiences with your product or service.

• It centers on the lived experiences of users, giving you a true-to-life image of understanding their needs, desires, and motivations.
• Captures the essence of consumer experiences, delivering authentic insights that can guide more empathetic and effective marketing strategies.
• The depth of phenomenological data can make analysis challenging. Working with thematic analysis and seeking expert advice can make it more manageable.
• Finding participants willing to share deeply personal experiences may be difficult. Offer assurances of confidentiality and create a safe, respectful environment.

Use phenomenological research to dive deep into the emotions and experiences of new market segments, refine user experiences for greater satisfaction, and create brand messages that forge stronger emotional connections with your audience.

10. Grounded theory method

The grounded theory method starts with data collection without a predefined hypothesis, allowing theories to emerge from the data itself. Through continuous comparison of data from interviews, surveys, or observations, you develop a theory that explains a particular aspect of consumer behavior or market trends.

• Exploring data without preconceived theories is ideal for uncovering fresh insights and new perspectives on consumer behavior.
• Based on the data, you can develop theories that explain patterns and relationships within your market, setting up a strong foundation for strategic decisions.
• As data collection and analysis proceed in tandem, you can refine your research focus based on emerging insights, ensuring the relevance and depth of findings.
• The open-ended nature of grounded theory means you’ll get piles of data. Using software for data management and employing selective sampling techniques to focus the research.
• The iterative process of coding and recoding data to develop a theory is complex. Training in grounded theory methods and regular team discussions can help clarify the process.

Use the grounded theory method to innovate products, tackle complex consumer issues, and craft strategies that deeply align with consumer preferences and behaviors.

11. Action research

Action research is a participatory method where researchers work alongside participants to identify and solve problems or improve practices. In the context of market research, it could involve collaborating with consumers to co-create solutions or enhance product design.

• Findings and insights can be applied in real-time, allowing for fast adjustments to products, services, or marketing strategies.
• Active involvement from participants, leads to a deeper engagement with your brand and a sense of ownership over the solutions developed.
• Balancing the input and engagement of participants without overwhelming them can be challenging. Set clear expectations and provide structured feedback.
• The focus on immediate solutions might overlook deeper, underlying issues. Supplement with other qualitative methods to provide a more comprehensive understanding.
• The cyclical nature of action research, with its continuous cycles of planning, acting, observing, and reflecting, requires dedication and flexibility. Agile project management techniques can keep the project on track.

Use action research to develop products informed by user feedback, enhance customer experiences through targeted improvements, and strengthen relationships with communities or stakeholders through collaborative engagement.

Conduct qualitative research when you need in-depth understanding of consumer attitudes, feelings, or behaviors—areas where quantitative research’s numbers and statistics can’t provide the full picture.

Qualitative research is best used in tandem with quantitative research – they really do compliment each other. You can use qualitative research to help inspire you at the beginning of a project, or to flesh out ideas that emerge during preceding quantitative research.

It’s especially useful for exploring new concepts, enhancing product development, or deepening brand engagement, complementing quantitative data by adding context and depth to the insights gained.

With Attest’s market research platform, you can seamlessly blend qualitative and quantitative data, giving you the insights you need for smarter marketing and better product development. See how Attest is helping businesses in a variety of industries to better understand their audiences.

Andrada Comsa

Principal Customer Research Manager 

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What is Qualitative in Qualitative Research

• Open access
• Published: 27 February 2019
• Volume 42 , pages 139–160, ( 2019 )

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• Patrik Aspers 1 , 2 &
• Ugo Corte 3  

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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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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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Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre

• Hassane Alami 1 , 2 , 3 , 4 ,
• Pascale Lehoux 1 , 2 ,
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BMC Health Services Research volume  24 , Article number:  701 ( 2024 ) Cite this article

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Artificial intelligence (AI) technologies are expected to “revolutionise” healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital.

Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework.

Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise.

Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients’ digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors’ priorities and the needs and expectations of healthcare organisations and systems.

Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

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According to the Organisation for Economic Co-operation and Development (OECD), artificial intelligence (AI) refers to “a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment” [ 1 ]. Unlike conventional software, many AI systems indeed have learning capabilities and self-correcting error mechanisms that allow them to improve the accuracy of their results based on the feedback they receive [ 1 , 2 ].

There are many application areas for AI in healthcare, for example: diagnosis, treatment, monitoring (e.g., chronic diseases), and patient compliance [ 3 ]. In certain experimental settings, AI technologies have been shown to be more effective than clinicians (e.g., diagnostic accuracy, more personalised diagnostics) [ 4 , 5 , 6 , 7 ]. Several have already been approved for clinical use in real-world care and services [ 8 ]. These technologies are seen as a lever for evidence-based clinical decision-making and practice and for value-based care and services [ 9 , 10 , 11 ]. Research indicates their potential to contribute to better monitoring, detection, and diagnosis of diseases, to the reduction of clinical risk, and to the discovery of new drugs and treatments [ 4 , 9 , 12 , 13 , 14 ]. The use of AI technologies could help to reduce diagnostic and therapeutic errors [ 2 ], contribute to the optimisation of clinicians’ work, and help reduce waiting times by reorganising clinical and administrative tasks, and supporting coordination [ 10 , 14 ]. Many scholars also argue that AI technologies could contribute to reducing healthcare costs by decreasing hospital (re)admissions, medical visits, and treatments [ 14 , 15 ].

A predominant and enthusiastic discourse in the academic literature and media reports is that AI technologies will revolutionise and radically change healthcare in the coming years [ 2 , 16 , 17 , 18 ]. There is an explosion of AI offerings in the market [ 19 ]. In 2018, the global AI market in healthcare was valued at around US$1.4 billion and is expected to grow to US$17.8 billion by 2025 [ 14 ]. In North America, the market for AI in healthcare had exceeded US$1.15 billion by 2020 [ 14 ]. In this context, healthcare organisations and systems are increasingly being solicited (or even pressured) to integrate these technologies, even when evidence of real clinical added value is lacking and many social and ethical as well as adoption, routinisation, and practical issues remain to be clarified [ 16 , 18 ]. According to Topol (2019), who reviewed healthcare workforce readiness for a digital future: “Despite all the promises of AI technology, there are formidable obstacles and pitfalls. The state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care” [ 4 ]. Liu et al. (2019) reported that few published studies on AI had results from real-world healthcare contexts [ 20 ]. These findings were corroborated during the COVID-19 pandemic [ 21 , 22 , 23 ]. Wynants et al. (2020) identified 232 AI models for prediction or diagnosis of COVID-19, none of which were appropriate for clinical use and only two showing potential for future clinical use [ 24 ]. Roberts et al. (2021) analysed 415 AI models for COVID-19 detection and concluded similarly [ 25 ].

This gap between the promise and reality of AI technologies in healthcare could be explained by the fact that efforts have historically focused on technology development, market penetration, and commercialisation. Limited work has been done to look specifically at the conditions and factors necessary for the integration of AI technologies into routine clinical care [ 14 , 17 ]. While technical problems (e.g., performance, unreliability) have been regularly put forward as a reason for the difficulties of integrating these technologies into healthcare organisations and systems [ 26 ], they explain only a small part of the problem. Broader socio-technical conditions and factors rather explain many of these difficulties [ 18 , 26 ].

The social scientific literature on health innovations has shown that the introduction of technologies into healthcare organisations and systems is a complex phenomenon [ 27 ]. This is particularly true for many AI technologies, which are sometimes described in the medical literature as disruptive innovation due to their evolving and autonomous nature [ 28 , 29 , 30 ]. Their implementation and use may require rethinking and/or redesigning existing governance frameworks and care models as well as new clinical, organisational, regulatory, and technological processes, business models, capabilities, and skills [ 18 ]. These changes involve, and impact on, a variety of stakeholders who may have divergent or even antagonistic expectations, goals, and visions towards technology [ 31 , 32 , 33 , 34 , 35 , 36 ].

To contribute to addressing current knowledge gaps, the goal of this study is to explore and understand the challenges of integrating AI technologies within a large academic hospital in Canada (referred to as “the City hospital”). We aim to answer two questions:

How do multiple interacting influences facilitate and constrain the integration of AI technologies within the City hospital?

What learning can we derive for policy and practice for better integration of AI technologies in healthcare organisations and systems?

The study is not limited to a specific AI technology or clinical area but encompasses all 87 AI technology-based initiatives developed and used to varying extent in this hospital. Where relevant, we specify the type of AI involved to contextualise the factors, conditions, or challenges described.

Theoretical framework

To make sense of the complexity underpinning the AI integration efforts in the City hospital, we used an adapted version of the Nonadoption, Abandonment, and challenges to Scale-up, Spread, and Sustainability (NASSS) framework developed by Greenhalgh et al. [ 27 ], which supports an exhaustive sociotechnical approach to health innovation. Following this adapted version, we present the seven dimensions of the framework in a different order from the original version in order to make sense of the narrative within the organisation studied, thereby covering: 1) the organisation; 2) the condition(s) or illness; 3) the technology or technologies; 4) the value proposition; 5) the adopter system(s) (e.g., staff, patient, caregivers); 6) embedding and adaptation over time; and 7) the wider system [ 27 ]. See Fig.  1 for a description of the seven dimensions.

An adapted version of the NASSS framework (adapted from Greenhalgh et al. [ 27 ])

There were many reasons for adopting the NASSS framework over other frameworks. First, it stems from a hermeneutic systematic review, supported by empirical case studies of technology implementation in healthcare [ 27 , 37 ], and its key strength lies in its synthesis of 28 technology implementation frameworks, that is informed by several theoretical perspectives [ 27 , 37 ]. Second, it was developed to fill an important gap “on technology implementation—specifically, to address not just adoption but also nonadoption and abandonment of technologies and the challenges associated with moving from a local demonstration project to one that is fully mainstreamed and part of business as usual locally (scale-up), transferable to new settings (spread), and maintained long term through adaptation to context over time (sustainability)” [ 27 , 37 ]. Third, in contrast to the deterministic logic of many existing frameworks, the NASSS framework is characterised by its dynamic aspect, particularly in terms of interaction and adaptation over time. Indeed, a large part of the literature in the field has a tendency to “assume that the issues to be addressed [are] simple or complicated (hence knowable, predictable, and controllable) rather than complex (that is, inherently not knowable or predictable but dynamic and emergent)” [ 27 , 37 ]. Therefore, major failures of large and ambitious technology projects may be underestimated and their complexity for healthcare organisations and systems tossed away [ 27 , 37 ]. Fourth, whereas decision-makers and technology promoters as well as a part of the specialised literature often adopt a linear, predictable, and rational vision of change [ 38 ], the sociotechnical stance of the NASSS framework highlights the importance of examining how technology and the changes associated with it are perceived, interpreted, negotiated, and enacted by individuals and groups [ 33 , 39 , 40 ]. The same applies to AI technologies that may require transformation and/or redesign of services, a profound reconfiguration of clinical and organisational practices, and challenges to professional identities and practices [ 17 , 33 , 40 ]. Certain types of AI technologies also evolve autonomously over time – a particular characteristic that can be explicitly conceptualised through the NASSS framework [ 27 , 41 ]. Overall, the NASSS framework was developed to be used reflectively, to stimulate conversation and generate ideas, which is one of our study’s aspirations.

We conducted a qualitative study within the City hospital (Quebec, Canada) [ 42 ]. The latter had initiated several projects to integrate AI technologies in its care and service offer. Decision-makers and managers expressed a need for (independent) insights into the micro-, meso-, and macro-level systemic implications of the integration of these technologies within the organisation [ 43 ].

Presentation of the organisation

The City hospital is one of the largest academic hospitals in Canada. It offers specialised and sub-specialised services to adult patients. It treats around 500,000 patients annually. It employs over 14,000 people. It also houses one of the largest medical research centres in the country, with an academic mission to produce and disseminate knowledge and research results. It also presents itself as an organisation with state-of-the-art facilities and equipment. It has been ranked by the U.S-based magazine Newsweek as one of the world’s top 250 Best Smart Hospitals for 2021. It hosts one of the largest annual digital innovation events in Canada.

At the time of the study, the City hospital had over 115 digital health projects (Table  1 ), with 87 of these involving AI. Around 95% (≈82/87) of the AI technologies were in the development/experimentation or early implementation phase. Only four were integrated into services. Approximately 72% (≈62/87) of the AI technologies identified within the organisation were for the diagnosis, treatment and/or monitoring of complex chronic or acute conditions: cancers, neurological (e.g., epilepsy), and ocular conditions.

Recruitment

We identified a purposive sample of key stakeholders, with the aim of capturing diverse perspectives and experiences [ 44 ]. We conducted internet searches and consulted reports and documents produced by the City hospital to identify potential participants, who were drawn from distinct roles and varied levels of involvement in the development, implementation, and use of AI technologies.

A personalised invitation email was sent to each potential participant explaining the project and why they were invited to participate. Two reminders were sent in case of non-response. Respondents were invited to indicate other participants (i.e., snowballing) [ 45 ]. This resulted in a sample of senior and middle managers/decision-makers, clinicians (e.g., physicians, nurses), clinicians/informaticians/researchers, technology assessment specialists, procurement specialists, lawyers, patients, and technology providers. Patients were identified through patient partners (volunteers) collaborating with the City hospital. Of the 42 invitations sent, 29 people agreed to participate. Table 2 shows participant profiles, many of whom cumulated multiple professional and/or experiential backgrounds.

Data collection

Between March and July 2021, the first author (HA) conducted 29 interviews in French (27) and English (2), using the Zoom™ videoconferencing platform (interviews lasted 30–90 min). Prior to the interview, a consent form summarising the objectives of the project was shared. Interviews were audio recorded with the permission of the participant and transcribed verbatim by HA. The questions were formulated according to the dimensions of the NASSS framework and informed by documents shared by the City hospital (e.g., list of projects and technologies). HA first tested the qualitative interview guide with two respondents prior to the start of the study. No major revision of the initial version of the guide was required. He took notes during and after the interviews and subsequently used them to contextualise the analyses. The interview guide slightly evolved depending on the participants’ responses as new information emerged. By adapting the interview guide, we were able to capture both expected and unanticipated tensions and practical challenges, grounding the discussion in participant experiences to avoid vague or abstract responses. Given that the same person (HA) co-developed the guide and conducted the interviews in French and English, this minimised the risk of variability that could arise from having different people collecting data in different languages. Interview data and document analysis, alongside our knowledge of the context (team members have been involved in various research and evaluation projects on digital technologies and innovations in Quebec and Canadian healthcare organisations and systems for several years) guided triangulation of data sources [ 46 ].

Data analysis

Data were coded and analysed with Dedoose™ software. HA performed the first round of analysis and developed a preliminary coding scheme. In the second round, the scheme was refined, challenged, and discussed iteratively by the second author (PL) [ 43 ]. We conducted a deductive-inductive thematic analysis. The deductive analysis was guided by the NASSS framework (Fig.  1 ) [ 27 ]. Drawing on its seven dimensions, we created codes to capture the micro, meso and macro-level challenges and implications associated with the integration of AI technologies in the City hospital. The inductive analysis aimed to capture emerging themes not covered by the framework [ 44 , 47 ]. After agreeing on the different themes identified, we concluded that none required the addition of new dimensions, as all identified themes fitted within the NASSS framework. Data saturation was reached for the themes and observations reported in the findings. Given the importance of context in the NASSS framework, we sought to understand and clarify the contextual elements where respondents had different views or judgements. We decided not to disclose certain details either because the participants requested it or to ensure confidentiality. However, this information was useful to contextualise and better understand other findings and events. Our findings are illustrated with participant quotes organised around key themes of the NASSS framework (translated from French to English when needed) (Table 5 in Appendix ). The letter P used in quotes refers to “participant”, followed by numbers designating the order in which interviews were transcribed.

Findings are reported as a narrative account [ 48 ]. This is critical in allowing us to capture the complexity of the subject, the explanatory and interpretative dimensions, and the varied stories and perspectives gained from participants in making sense of the issues around the adoption of AI technologies.

We present the findings according to the seven dimensions of the adapted version of the NASSS framework (Fig.  1 ). To ensure fluidity in the presentation of the findings, the participant roles are used as a general category to help the reader identify certain tensions between the viewpoints and perspectives expressed. In this sense, there is no pretension of generalisation given the small number of respondents in each category. The analyses are intended primarily to provide high-level dynamics related to each dimension of the NASSS framework and not those specific to the types of AI discussed.

The organisation

For the technology providers we interviewed, the City hospital has several internationally renowned clinicians, both in the clinical field and in the use of AI. Several managers and clinicians also reported that senior management is known to value and encourage technological innovation, which has led to the creation of a “data lake” that allows the integration of data from different clinical systems (e.g., clinical records, laboratories, vital signs, imaging), which is a major asset for the development and/or validation of certain AI technologies. According to technology providers, access to the specialised expertise of clinicians who know the data is as important as access to the data itself. These clinicians play an important role as a trusted guarantor (or legitimising authority) for AI with other clinicians, decision-makers/managers, patients, and citizens. In the words of one clinician-manager, the relationship and communication between these clinicians and the City hospital’s senior management is generally perceived as positive. He pointed out that this synergy helps to mitigate some of the issues and conflicting visions and expectations of AI.

According to a technology provider, because of the characteristics of Quebec’s single-payer and universal health system, the City hospital allows for holistic management of patients suffering from several pathologies or requiring different care and treatments. He added that this unique advantage enables the development of AI technologies with a broad spectrum of action (i.e., compared to those developed in contexts where care is fragmented between different hospitals and/or clinics). Despite this asset, there is a broad agreement among the interviewees that the City hospital is characterised by significant complexity that has the potential to impact its ability to realise the value promise of AI technologies.

Use of AI technologies in the City hospital necessarily involves different departments, committees, and stakeholders (e.g., Information technology -IT- department, procurement department, project office, professional services department). According to several managers, clinicians, and industry providers, the roles and mandates for these different groups and stakeholders are not always clear. Coordination and communication between teams and/or departments are sometimes difficult or non-existent. According to a manager, this results in confusion and tension about expectations, visions, and responsibilities. He pointed out that difficulties experienced by some AI projects were due to a department or committee not being engaged at the right time (e.g., as a result of legal and/or procurement framework, Cloud storage space, professional services department). For managers and clinicians, a horizontal body should have been established to coordinate and ensure coherence and communication between the different initiatives and stakeholders, with the aim enabling mutual effort, coordination, and accountability. For another manager, by ensuring an initial screening of technologies proposed by industry, such a body would avoid the influx of useless technologies to clinical teams and associated time and resource costs.

Both industry and organisation respondents agree that the City hospital doesn’t always have the capacity to meet the initial and recurring costs and investments required for the successful integration of AI. To overcome this funding problem, at least partly, an interviewee told us that the organisation is obliged to open its doors to industry for co-development, or as a testing ground, of AI technologies. This sub-contracting allows the City hospital to benefit from a free user licence for a fixed period or for life. However, it was reported that this partnership contracts model (e.g., co-development or serving as a testing ground for the industry) is likely to lock the organisation into a technology-centric logic, with no real margin of manoeuvre to choose technologies that really meet its needs. There are multiple projects under this partnership model within the organisation. Several technologies could simply end up being only partially developed because the technology provider has withdrawn, or the technology was abandoned. Within such a context, several managers and clinicians recognise that it is difficult to create a real organising vision that supports and enables AI within the City hospital.

According to managers and clinicians, these partnerships with industry imply an over-solicitation of the clinical teams as, in addition to their clinical and administrative work, they must dedicate time to testing and experimenting with the various technologies presented by the technology providers. In this regard, several organisation and industry respondents pointed out that clinicians in the City hospital are not valued or remunerated for their contribution to the development and/or experimentation of technologies. It is not uncommon for some clinicians to feel that industry benefits from their clinical expertise without any real return on investment for them. Technology providers interviewed refuted this point. For them, the difficulties in integrating their technologies into the organisation are essentially due to the opposition of some influential clinician-researchers who are themselves developing in-house similar technologies. In the words of one industry respondent, this is a conflict of interest and unfair competition. Nonetheless, technology providers support the importance of creating incentives to encourage clinicians to collaborate with industry. On their part, several clinicians and managers consider that the organisation should value in-house initiatives more highly because they emerge from the needs and expectations of the field. However, there is agreement that the organisation does not have the financial and human resources to support these initiatives. In addition, according to one manager, as a public entity, the City hospital does not have a mandate to develop and/or commercialise technologies. At some point, a company would have to be involved to ensure commercialisation.

Managers, clinicians, and industry acknowledge that the nature and extent of the changes associated with the integration of AI within the organisation are still largely unknown. For example, it is very difficult to assess financial implications over time. Two managers reported that the City hospital paid an additional CA$20,000 to CA$30,000/year for the storage and management of its data. This cost was not initially budgeted but subsequently required by the Cloud service provider who had estimated the size of the data. According to the same respondents, such “little surprises” could lead to some technologies being abandoned along the way, even if they are clinically relevant, either because the organisation cannot afford the costs or the Quebec’s Ministry of Health and Social Services (known as MSSS) refuses to cover them.

Both industry and organisation respondents reported that many AI technologies require access, sometimes in quasi-real time and without human intervention, to large amounts of data of various types. Unanimously, interviewees acknowledge that the organisation’s rules and procedures do not currently allow this (or very barely). Technology providers are calling for easier access to data. However, on the organisational side, several managers consider that such rules and procedures need to be further strengthened. Some of them emphasised the importance of having a Specialist digital lawyer to ensure that these issues are addressed when contracts are signed. They also add that there should also be a Chief data officer to ensure adequate and coherent governance between the various initiatives that involve clinical-administrative data.

The condition(s) or illnesses

Most of the AI technologies identified (72% ≈62/87) within the City hospital are directed at the diagnosis, treatment and/or monitoring of complex chronic or acute conditions (e.g., cancers, neurological, ocular conditions) (Table  1 ). These conditions generally require ongoing or periodic support and monitoring over long periods of time with significant implications for patients and their families, and for the financial sustainability of the healthcare system. They also require complex, individualised, and evolving service models to continue to meet the needs of patients and their families. Several interviewees underscore that the use of AI could reduce waiting times and the costs of managing these pathologies. For a technology provider, these technologies are also expected to help identify new patterns and digital biomarkers that would facilitate the diagnosis and treatment of poorly characterised and/or unpredictable diseases.

For several respondents, this focus on specific diseases is partly due to the nature of the technologies available on the market. These technologies are addressing pathologies mainly through image analysis and/or signal quantification. This makes them more easily measurable, therefore more attractive to technology developers seeking rapid market access.

The technology or technologies

There are diverging perceptions between clinicians, managers, technology providers, and patients on what makes AI attractive, reliable, and mature enough for clinical use and/or interoperable with existing systems.

According to a manager, some of the technologies proposed to the City hospital under the label “AI” are, in fact, expert systems with advanced calculation software. Branding the products in this way is a strategy used by some companies to attract investment and/or obtain contracts. While an AI designation increases the market value of the technology, it does not necessarily increase the clinical value. For another manager, this labelling of AI products is also partly due to the organisation’s pressure on technology providers to integrate AI. This is a significant step for technology companies as, compared to traditional software, AI technologies require specific regulatory requirements, technical infrastructure, expertise, and resources.

Several participants raised emerging security issues specific to AI. This is not only about the security of the technology and infrastructure, but also about the security of the algorithm itself. The latter could be hacked and modified, which can have a direct clinical impact on the patient. According to a manager, being able to recombine data from different sources, AI technologies could easily re-identify individuals. On their side, technology providers pointed out that these security issues are mainly due to the City hospital’s obsolete systems and technology infrastructure. They underscore how their technologies conform to the best security and quality standards and norms on the market, and that unlike public organisations they have the best IT expertise. An industry respondent added that, since the customer is the guarantor of their added value on the market, they also regard data security as central to their reputation and brand image. If an incident occurs, the company could simply lose customers or even go bankrupt.

Some AI technologies need to run on an integrated technological platform or operating system (e.g., electronic health record -EHR-) that allows for optimal data flow and exchange between the different technological systems and organisational departments as well as across healthcare system organisations. Respondents agree that the City hospital’s departments generally have outdated and disparate systems and infrastructures that are frequently not interoperable. However, several managers, clinicians, and technology providers argue that this is a common problem for the whole healthcare system, as an integrated and interoperable EHR does not exist. In this regard, for a population of over 8 million people in Quebec, there are over 30 million patient identification cards. A patient may have several cards with a fragmented EHR in several organisations. Similarly, one interviewee stressed that the equipment used (e.g., scanner, magnetic resonance imaging -MRI) in the City hospital does not always meet the requirements for AI. In some situations, it is difficult to know where the data is, or how it is processed and collected by certain technologies or equipment. Problems with internet connection and data transmission via Wi-Fi are also reported.

There is a consensus that AI technologies need high-quality data. Both industry and organisation respondents highlighted that a significant amount of clinical-administrative data (e.g., handwritten clinical notes) and patient records are still scanned in portable document format (PDF), which is not usable for planned AI. For technology providers, the meaningful use of data, which raises the question of the purpose of the data collection, is missing within the organisation and should be given more consideration.

For its AI programme, the City hospital works with many specialised start-ups and small- and medium-sized enterprises (SU/SMEs). One such technology provider stresses that the survival of their company depends on their ability to seek liquidity in the financial market (e.g., venture capital). This means that they are necessarily accountable to their shareholders who may be looking for the fastest and most profitable exit events possible (i.e., when an investor sells his/her shares in a company to collect cash profits). This approach brings challenges for the City hospital in terms of working relationships, technology development, and continuity of care. For instance, SU/SMEs can be bought by multinationals or simply disappear (e.g., bankruptcy), or a company may stop a technology or cease to update it. According to a manager, the City hospital does not necessarily have the capacity to maintain these technologies on an ad hoc basis or replace them with others. Another interviewee added that sometimes the organisation has no guarantee of recovering data hosted or operated by these technology providers or their subcontractors (e.g., Cloud services).

The value proposition

Stakeholders interviewed have divergent definitions of what constitutes the perceived, anticipated and/or actual value of a technology and the parameters to be considered for measuring it (e.g., safety, efficacy, and effectiveness criteria). About 95% were still in development/experimentation or implementation.

Several technology providers mainly express the value of their technology in terms of its potential to improve healthcare and its efficiency. They pointed to significant consumption of resources by the healthcare system while at the same time being unable to meet the healthcare needs of the population. For these interviewees, AI can solve the problem whilst modernising the healthcare system. In this regard, for a supplier, to realise such value, the City hospital, and the healthcare system in general, must be willing to take some risks. He stressed that if the latter wait for AI to be perfect and risk-free before using it, the technology will never be integrated, and its value promise never delivered to the population.

A manager reported that many AI technologies in the City hospital were at a value promise stage (i.e., with anticipated, rather than actual value stage). Other interviewees consider that this value promise remains relatively speculative, based on vague projections and estimates. In this regard, from the organisation’s perspective, the perceived value of AI technologies is mainly about improved clinical quality and safety, and performance. The expectation to achieve this value is to have tailor-made AI technologies adapted to the setting, clinical contexts, and ways of working. However, focusing on tailored AI solutions can sometimes be a major constraint for technology providers. According to several interviewees, suppliers prefer to commercialise generic technologies that can be easily marketed elsewhere with minimum modification (plug-and-play). Several managers and clinicians added that the costs involved in implementing and adapting the technology to the local context are regularly underestimated by these suppliers. The latter often lack an understanding of the complexity of clinical practices. For example, one company stopped working with the City hospital because it considered that its clinical needs are too specific for the AI technology to be cost-effective.

Because of its status as a leading academic hospital, the City hospital is highly sought after by the AI industry. Several interviewees recognise that the organisation is used to showcase and legitimise the technology’s value proposition, hence its market value and potential for widespread commercialisation. A technology provider also reported that the organisation serves as a gateway to the healthcare systems of Quebec and other Canadian provinces. At the same time, according to organisation respondents, the City hospital benefits from media coverage, which gives it a competitive advantage in attracting talent and expertise. However, divergence over the actual added value of certain technologies may constitute a source of tension between senior management and clinical teams. Some AI technologies are likely to exacerbate workload and staff burnout (e.g., technologies intended for the optimisation of clinical-administrative processes). For a manager, since AI technologies are still considered over and above other priorities, their impact on the quality of work and clinicians’ satisfaction is not really taken into consideration in the organisation’s assessment of their value (e.g., flexibility, alignment with clinical-administrative workflows). He added that the City hospital has difficulty in moving the value of these innovations from the Triple Aim to the Quadruple Aim: “improving the patient experience, the population health and the quality of work and satisfaction of healthcare providers, and reducing costs” [ 49 ].

The organisation’s clinical-administrative data, which is used to develop and/or operate some AI technologies, may contain biases and may not be representative of the general population. For several interviewees, AI technologies may also not respond to the contextual realities and needs of some populations (e.g., indigenous, rural, or minority people). Patients and organisation respondents also pointed out that these populations are rarely involved in the design, development, and implementation of AI technologies within the City hospital. Several interviewees recognise that assessing the added value of AI technologies by population segment is essential, but very difficult to achieve.

The adopter system(s)

Interviewees overwhelmingly agree that certain AI technologies could have a direct impact on the patient-clinician relationship. Some progressive diseases require human care and support over time. For AI technologies designed to monitor chronic diseases, some patients fear being lost from sight by their healthcare providers. According to several patients, it is important to ensure that they always have the possibility of in-person meeting with their clinician. As a patient pointed out, technology could never understand their subjective experience with the disease better than the clinician. For this and another patient, listening and empathy are sometimes more important in a care pathway than medication and technology. They mentioned that the therapeutic relationship goes beyond the simple dimension of the disease.

According to a patient, some patients registered with the City hospital can have up to 5 technology applications, sometimes non-interoperable. Some of these technologies do not operate on older Apple- or Android-supported smartphones, making it hard for several patients to use them unless they upgrade their hardware. Technologies may also require access to patient-generated data at home. Patients, clinicians, and managers stressed that patients may not have the technology and equipment and/or a good internet connection, but also the social and cultural capital (e.g., literacy, family network) to fully benefit from the potential of these technologies. They recognise that these technologies could lead to additional costs and expenses for these patients. Even when they have the technology, they may need technical support at any time of the day (24/7) as the disease “has no working days”, as a patient notes. This support is not automatically provided by the organisation and not all patients have a family/friend network that can be mobilised when needed. Paradoxically, technology could exacerbate the disease burden for these patients.

Several respondents reported that the adoption and use of certain AI technologies typically requires a reorganisation, or even a redesign, of clinical practices, of the organisation of services, and of the modes of governance and control within the City hospital. According to clinicians and managers, these changes could be associated with a feeling of loss of professional autonomy, identities, values, and skills. In the words of a manager, AI technologies could cause an erosion of information asymmetry (in favour of the organisation and the MSSS) and challenge clinicians’ autonomy of practice. The erosion and reduction of the scope of expertise due to the replacement of part of the clinical activity by AI was also pointed. However, several respondents relativised these fears, stressing that it is rather the clinicians trained in AI (e.g., clinician-informatician, clinician-data scientist) who will replace the others. This new expertise will have to be institutionalised and valued. This could imply a revision of the boundaries of professional jurisdictions (e.g., reserved acts) and of certain negotiated orders and privileges, and therefore of powers (e.g., nurse vs. general practitioner; general practitioner vs. specialist physician). Managers and technology providers pointed out that a technology that provides real added value for patients will never be integrated into practice if clinicians perceive it as a threat to them.

It was reported that the effort to integrate AI within the City hospital is occurring in a context where clinicians are under great pressure with high workloads. Some emphasised that they have no time to waste on these technologies, particularly those imposed on them by senior management and/or industry. They also expressed a feeling of innovation fatigue. Managers and clinicians acknowledge that this lack of time, but also of engagement, has a negative impact on the success of technology training and promotion initiatives within the organisation, and therefore its subsequent adoption and use. In addition, clinicians involved in technology integration efforts are mainly volunteers (e.g., champions, super-users). As the contribution to innovation is not considered a clinical activity, it is not remunerated nor recognised in their performance indicators. According to several clinicians and managers, this point is a significant barrier to clinicians’ engagement, especially to embrace the necessary changes and adaptations, and to construct meaning and develop new identities with regards to AI.

There is agreement that the need for continuous monitoring and follow-up of some AI technologies in everyday clinical practice made the role of IT teams more critical to clinical practice. According to a manager, this is a major change as clinical and IT teams have historically evolved in silos. In this regard, it is difficult to align cultures and languages within the City hospital in the midst of developing AI technologies and services. For some clinicians, the increasing adoption of AI in their practice may make them dependent on IT teams (potentially conflicting with their autonomy of practice). To address this issue, an interviewee emphasised the importance of the presence of translators or boundary spanners with a hybrid clinical-IT profile to bridge and build a healthy collaborative space between clinical and IT teams. These translators could also act as a bridge between clinical teams and technology providers. The same respondent reported that such a role is already played by members of the City hospital’s Innovation Pole team and several clinicians.

Several managers and clinicians, acknowledge that the blind confidence and lack of critical distance could affect the use of certain AI technologies in clinical decision-making. In this regard, they see the problem of transparency and explainability of AI decisions (black box). According to an interviewee, the problems of data quality and bias are serious enough to be doubly vigilant on this point. A technology provider recognised the importance of clinicians being able to understand how the decision is made by the AI (e.g., parameters retained or excluded) and whether such a decision is right or wrong. To do so, clinicians may need technical support from AI experts, which the City hospital does not necessarily have. According to several respondents, it is difficult for public organisations to recruit AI experts, as the latter are more attracted by the private sector where working conditions and remuneration are very advantageous.

Embedding and adaptation over time

The City hospital’s IT systems are theoretically well secured for AI or associated technologies needed for its functioning. Indeed, any new technology for clinical-administrative use should meet strict criteria for safety and effectiveness. They should be licensed and/or authorised by the IT department or regulatory agencies. However, several managers and clinicians recognise that, once implemented, numerous technologies are not necessarily monitored and controlled over time. The result is a complex, fragmented, and non-interoperable technology environment that is difficult to manage and update, but also vulnerable to cyber-attacks. Some AI technologies are likely to dysfunction and/or operate and evolve awkwardly in such an environment, which could pose patient safety issues.

According to industry, clinicians, and managers, the lifecycle of AI technologies (i.e., the period during which they can function adequately without major upgrades and avoid replacement by new and better technologies) is often very short, and potentially only a few months. The City hospital should be able to upgrade its technology systems and equipment continuously. The costs can be significant. In this regard, equipment and devices (e.g., scanner, MRI) required for the functioning of certain AIs may be considered obsolete after only five years of use. The data they generate is no longer usable, which has a direct impact on their clinical reliability (e.g., ability to detect cancer). To remedy this problem, some technology providers offer to lease equipment. According to the latter, City hospital could then benefit from the latest equipment, with embedded AI, with no obligation to purchase. A technology provider explained that such a model involves the organisation to engage in service contracts over varying periods of time with the supplier. Such contracts usually include the implementation, maintenance, and upgrading of the equipment and associated technologies. The same respondent emphasised that this proximity model would also allow for a feedback process, necessary to adapt to the evolving needs and expectations of clinical teams. However, for several managers, this model raises concerns about the risk of locking the City hospital into a dependency relationship with a single supplier. They reported that this “chaining” could, among other things, increase the supplier’s control of the organisation’s data. To illustrate this point, an interviewee indicated that a technology provider has already “forced” the City hospital to pay for access to its own data (hosted/stored on the supplier’s servers). The same person reported that suppliers want to benefit from an annuity/rent, i.e., a continuous flow of money over time.

The wider system

A gap exists between those who call for a pragmatic approach (e.g., test-and-error, sandbox logic) and those who call for the consolidation of the precautionary principle (i.e., decision-makers adopt precautionary measures when scientific evidence about a human health or environmental hazard is uncertain and the stakes high) [ 50 ]. For several suppliers, the precautionary principle is a major obstacle to the integration of these technologies into the healthcare system. They stressed that regulation should be made more flexible, because zero risk does not exist in healthcare. An interviewee pointed out that the autonomous and evolving nature of some AI technologies will inevitably lead to failures and unforeseen incidents. Instead, lessons should be learned from these malfunctions and incidents to improve the technology. The Post-Market Approval/Post Market Surveillance model adopted in the USA was given as an example. This approach is rejected by other several managers and clinicians who consider that the lives and safety of patients cannot be subject to “hazardous test-and-error”.

Respondents are unanimous in stating that the authorisation, contracting, and financing process of AI technologies by the MSSS, which mainly focuses on the initial purchase price (capital equipment, which results in the procurement of technology with a fixed price, often the lowest, of which the organisation becomes the owner), is no longer adapted to the reality of AI technologies (Table  3 ). Firstly, many AIs operate with a “Software as a Service (SaaS)” business model. It is a monthly or yearly subscription for the organisation. According to technology providers, this model is justified by the fact that these technologies require continuous monitoring, control, and maintenance over time. Some respondents also called for the adoption of the “Value-Based Procurement (VBP)” business model. In this case, the suppliers are paid according to the value generated by their technology (e.g., 10% of the savings made over a patient’s entire care and service cycle). As these technologies are not cheap, there is a risk that they could be excluded from current tendering processes. According to several managers, the tender model does not consider the costs required for the implementation and adaptation of the technology to the local context. Examples where additional costs were required at the time of implementation, not initially foreseen, are relatively common. However, interviewees recognise that VBP is still difficult to implement. Because of the evolving nature of certain AIs, their value could change over time. Currently, it is difficult to ensure their continuous evaluation and monitoring due to the fragmentation of services and the lack of an integrated EHR, as well as trained and qualified human resources (e.g., collection, organisation, structuring, visualisation, and analysis of AI technology usage data), among other things.

According to several managers, the difficulty of acquiring certain AI technologies through the tendering process is another reason why the City hospital prioritises partnership contracts (e.g., co-development or serving as a testing ground) over service contracts (e.g., procurement of technology and/or associated services) with suppliers. In the words of a manager, as long as the organisation does not incur expenses (e.g., having the technology at no cost for a given period or forever) from its operating budgets, it does not have to justify its actions to the MSSS. This strategy also allows the City hospital to accelerate the integration of these technologies into its care and service offer by avoiding the complex bureaucratic process of the MSSS. However, some interviewees reported that partnership contracts do not always allow for the sustainable use of the technology beyond the free-of-charge period. In some situations, the organisation would have to incur expenses after this period and sign a service contract. It would then have to go through the tendering process again. If the latter is won by a different supplier, the initial technology should then be withdrawn, which condemns the City hospital to a kind of eternal restart.

Several technology providers argue that the tendering model is a barrier to entry into healthcare for SU/SMEs, although they could offer AI technologies with real added value. Unlike large companies, SU/SMEs do not have sufficient financial and marketing capacity to offer low prices.

Several respondents, both in the City hospital and industry, pointed out that the Act on the protection of personal information is also seen as a major obstacle to AI in the healthcare system. Typically, when a patient is treated in a public healthcare organisation, his/her consent does not include the secondary use of his/her data for research or other purposes. Legally, AI technologies developed or tested with this data cannot be used and/or commercialised, at least theoretically. According to an organisation interviewee, overcoming this barrier would entail considering that once a patient is treated in a public healthcare organisation, he/she automatically consents to the secondary use of his/her data for service improvement and research purposes. Several patients interviewed agree with this approach. However, they insisted that patients should always be able to withdraw their consent if they so want (opt-out).

Also concerning data, several interviewees highlighted the central role and necessity of Cloud services (e.g., data storage, exchange, and management) for optimal and effective use of AI technologies. According to a manager, Cloud services providers are mainly multi/transnational companies. The latter have servers and relay points all over the world, which means that data could travel across national borders. This challenges regulatory sovereignty. The same interviewee reported that Quebec legislation requires that data be hosted on servers located on its territory. However, the City hospital does not always have the levers to verify and ensure that the providers really respect this requirement. Nor does it always have the possibility of knowing whether an incident (e.g., security breach, data leakage) has occurred if the company does not communicate the information to it. In the words of another manager, “[The City hospital] does not always have the capacity to [ensure the security and reliability of the technologies], so it is forced to trust [the suppliers]”. In the same vein, it does not always have the levers and means to ensure that the technology provider has destroyed and/or deleted the dataset when requested to do so. In addition, according to another interviewee, the definition of responsibilities in the event of a patient harm incident is a not fully resolved issue yet. The latter highlighted that compensation could involve large sums of money that neither the supplier nor the City hospital would want to pay. In this regard, by simply being identified as a potential liable party in the event of an incident, the organisation or company could see the amount of its insurance contract increase considerably because of the risks involved.

Many AI technologies used in clinical decision making are considered as “Software as a Medical Device (SaMD)”. There is still no clear framework for their assessment and approval in Quebec and Canada. In addition, professional federations and colleges, and medical insurance bodies have not yet taken clear positions on their use in clinical practice. According to several interviewees, the absence of solid clinical practice guidelines, protocols, remuneration models, and professional responsibility frameworks limits the possibility of clinicians using these technologies. As an illustration, a manager pointed to the complexity of identifying responsibilities in the event of an AI error (e.g., misdiagnosis or mistreatment). Since certain technologies can decide autonomously, part of the responsibility of the clinician is transferred to them. For the same interviewee, numerous questions have yet to be answered: to what extent does the technology replace the clinician (totally or partially) or not? With the “black box” problem, AI does not always allow for tracing and understanding the decision-making process. Even when it is possible, technology providers might refuse to give access to their algorithm for commercial confidentiality and market competitiveness reasons. It is then difficult to know the nature and/or origin of the fault. Moreover, there is also the question of whether AI should imply an obligation of results, instead of the obligation of means to which clinicians are presently committed. According to another manager, technology providers prefer to classify their technologies outside the SaMD category. In this way, the clinician remains solely responsible in the event of harm. Then, the supplier avoids paying damages that may be substantial. Indeed, compared to a clinician’s error, which is usually limited to a single patient, an AI technology’s error could affect many patients. However, providers explained this choice by the fact that technology approval processes, such as SaMD, are time-consuming and very expensive.

Other regulatory constraints are pointed out by several interviewees. AI technologies never arrive ready for clinical use (plug-and-play). There is often adaptation and alignment work to be done. Some changes and/or adaptations are made informally (e.g., bricolage, workarounds) by clinicians. According to a clinician and a manager, these modifications are sometimes crucial in their decisions to use the technology or not. However, from a regulatory perspective, once licensed and authorised, a technology should not generally be modified, at least theoretically. Currently, any changes require the approval of the City hospital’s IT teams or of a governmental regulatory agency. Although justified in terms of financial and safety risks, there is a consensus among interviewees that this process is rigid, time consuming, and inadequate for the reality of AI. In this regard, updates to AI technologies should be quasi-automatic and continuous, in the spirit of how the iPhone works, often without human intervention. In the words of a clinician, any delay or blockage could have a direct impact on the diagnosis or treatment of patients.

According to a manager, aspects related to the organisations’ performance criteria and, therefore of their funding by the government are not yet fully defined for AI. In Quebec, the activity-based funding model is being deployed to complement the dominant historical budget model. This new model generally considers the activity of physicians (e.g., diagnosis, treatment, surgery), paid essentially on a fee-for-service basis, in the calculation of the budget the organisation will receive from the MSSS. The activity of other healthcare professionals, mainly salaried by the organisation (e.g., nurses), is not considered the same way (or only slightly) in these calculations. Numerous AI technologies intended for (or assisting in) diagnosis or treatment could be supervised by healthcare professionals other than physicians. The impact of this development on the funding of healthcare organisations remains unknown. In the same vein, the respondent highlighted the problem of the fragmentation of funding between medical, medico-social, and social services in Quebec. For example, some AI technologies have a clinical added value and are therefore covered by the MSSS. However, the latter does not cover other aspects such as the improvement of the patient’s quality of life (e.g., Quality-adjusted life year -QALY-). As a result, the City hospital could be required to solicit different departments, ministries and/or agencies to capture the different value components of the same AI technology.

According to several interviewees, funding from the federal government would have a direct impact on the integration of AI technologies into the City hospital. They report that federal programmes make it possible to fund expensive infrastructure projects, from several hundred thousand to several million CA$. However, implementation and sustainability are mainly under the responsibility of the Quebec MSSS because health falls under provincial authority in Canada. There is sometimes a gap between federal funding and provincial priorities. According to a manager, the Quebec MSSS does not automatically fund the implementation and sustainability of federally funded technologies. As a result, several technologies could eventually be abandoned. For another interviewee, one of the important limitations is that federal funding is often very targeted and specific to particular technologies and/or clinical areas. It does not provide sufficient flexibility for organisations to use it according to local needs and contingencies.

Lastly, several respondents recognise that inter-organisational collaboration for sharing expertise and experience is essential for AI. However, the fragmentation, lack of communication and coordination across public healthcare organisations make it difficult to establish such a collaborative environment. For example, according to a clinician, to develop AI technologies with real added value, it would be necessary to have access to large amounts of patient data. She explained that the way to do this, while competing with other technologies from other countries, is to pool the databases of different healthcare organisations in Quebec and Canada. Such an inter-organisational network is essential in the evaluation and approval process of AI technologies, as they are to be tested on data from different healthcare organisations (e.g., urban and rural hospitals, primary care clinics). For the same respondent, such multicentre testing would ensure reliability and effectiveness in different clinical and technological settings across the country.

Summary of key lessons

Our study aimed to generate a better understanding of the conditions that facilitate or constrain the integration of AI technologies in a large healthcare organisation in Canada. By analysing a rich corpus of data using the NASSS framework, the study highlights seven lessons:

Firstly, an organisational culture and leadership that creates favourable conditions for AI is essential as well as the presence of clinical champions who act as ambassadors for AI. This is a lever to attract clinical and/or technical talent and expertise, but also companies in the field. The strategic alignment of the organisation’s clinical-administrative processes and infrastructures with AI technologies remains a major challenge. A lack of alignment could lead to partial integration of technologies or their abandonment, resulting in innovation fatigue among clinical and administrative teams. In a context where clinicians are over-solicited, they should be given the time needed to integrate the change, but also develop the professional expertise and identities that AI could require. It is also important that the technologies proposed to them are supported by evidence of improvements in patient care and services as well as in their work conditions and quality. The integration of AI within a hospital also involves a multitude of stakeholders whose activities and actions should be coherent and synergistic. Communication is fundamental to clarify roles, responsibilities, and mandates and requires a horizontal structure capable of coordinating actions and shaping a consistent organisational story about AI. The technologies proposed by the industry should be filtered so that those that really meet the needs on the ground are prioritised.

Secondly, financial and other incentives are needed to encourage clinicians to experiment and adapt these technologies to their practices. Investments in the development of AI technologies have so far focused on specific complex pathologies that present a great burden to patients and their families as well as to the healthcare system. To address these pathologies, AI mainly exploits image analysis and/or signal quantification, which makes it easier for suppliers to develop technologies and introduce them more quickly to the market. Yet, the sensitivity of safety and data protection issues implies that the hospital hires a lawyer specialising in digital technologies (to ensure that contracts are properly made) and a Chief data officer (for adequate and consistent data governance). Upgrading IT systems and infrastructure and recruiting new expertise hence require planning for both initial and recurring investments and expenditures.

Thirdly, the interoperability of AI technologies and the organisation’s systems and infrastructure are major obstacles to their routine use. Some technologies need quasi-real time access to data, which requires an integrated platform to ensure optimal data circulation between different IT systems and departments of the organisation, or even other organisations involved in the patient’s treatment. The qualification of some advanced software as AI could have financial and legal implications for the organisation. In addition to traditional clinical safety issues, the AI algorithm itself could be hacked and modified, resulting in harm to patients. By recombining data from various sources, individuals could be easily re-identified. These technologies could also require high-tech equipment with very short lifecycles, which the organisation may not have. Furthermore, many AI technologies are driven by SU/SMEs that could disappear from the market at any time. Hence, organisations should have the capacity to maintain the technology on an ad hoc basis or find an alternative and be able to recover and/or ensure the deletion of data by the initial supplier.

Fourth, the definition of the value of AI technologies is far from consensual as well as the expectations regarding what they can or should do. The ability to measure this value is of considerable complexity given the great contrast between the value proposition stated by suppliers, and sometimes by managers, and the actual value to clinicians and patients. The value of AI is not self-evident. Indeed, even if it has shown great performance in a laboratory context, this may not materialise in the real-world context of care and services. The value of some AI technologies also contrasts with the risks they raise given their evolutionary and autonomous nature. There are trade-offs between the precautionary principle, the need for some risk tolerance, and its clinical potential. Moreover, clinical practice may require very specific AI technologies, whereas suppliers tend to prioritise plug-and-play technologies with a potential for widespread commercialisation. The global value of AI could vary widely depending on the balance of the changes and transformations it requires and what it actually provides. This value may also change over time. Evaluating and monitoring AI’s value on an ongoing basis requires resources and expertise the organisation may lack, especially in view of the (re)production of bias across sub-groups of the population.

Fifth, contrary to the rhetoric about their potential to humanise care, some AI technologies raise concerns about the patient-clinician relationship and, therefore, about quality of care. The risk of mechanisation of care and the difficulty of physically accessing healthcare providers is palpable. Digital literacy, technical support, and change management for clinicians and patients using these technologies are essential. For clinicians, AI technologies may imply redesigning clinical practice and service organisation, but also new governance and control strategies within the organisation. Although improbable, there is a real concern that AI could partially or totally replace the activity of clinicians. Hyper-dependence on technology raises concerns about the erosion of clinicians’ expertise and the risk of blind trust in the decisions made by AI. As a result, clinicians may worry about being subordinated to the IT teams that would play a central role in the production of care. This new reality highlights the central role of translators or boundary spanners in building bridges and trust between clinical and IT teams, but also with industry. On a larger scale, the technology-driven approach to AI could cause a deterioration in clinicians’ work conditions and quality.

Sixth, the evolving and self-learning nature of some AI technologies makes time critical, distinguishing them from previous licensed technologies that do not generally require a new approval review. IT teams should approve and validate any changes or adaptations, and this becomes difficult with some AI technologies that evolve autonomously and update themselves. Any delay or blockage could threaten the diagnostic or treatment quality of patients. Continuous monitoring and control over time is required to avoid malfunctions and incidents, but also to make the necessary improvements. In this regard, the increasingly short lifecycle of software and hardware challenges the technical and financial capacity of the organisation to adapt and evolve its systems, equipment, and infrastructure at the right pace. Evolutionary AI technologies create the need for close and sustainable relationships between the organisation and the technology providers, a new relationship that: 1) requires solid frameworks to identify and resolve conflicts of interest as they arise over time; and 2) must avoid lock-in and dependence upon a single provider.

Seventh, many socio-political, economic, and regulatory factors are decisive in the integration of AI technologies, which are mainly offered under SaaS and/or VBP business models. These models are in opposition to the current tender model in Quebec that emphasises the cheapest technology (capital equipment). The legal framework of the current model constitutes a barrier to entry for SU/SMEs, some with high value-added technologies. Established bureaucratic acquisition processes are inadequate for the very short lifecycle of AI technologies. Consent requirements for the use of patient data are misaligned with this new reality and are prompting consideration of an opt-out consent model. AI technologies increasing rely on Cloud services mainly offered by multinational companies with servers and relay points all over the world. Data governance is even more important as healthcare organisations and systems have limited resources and tools to ensure that data management and storage comply with applicable laws. Identifying liability in the event of harm could therefore be very complex. AI technologies classified as SaMD, on the other hand, have specific requirements for quality, efficiency, and clinical reliability. To date, the lack of reference technologies makes it difficult for regulatory agencies to assess and approve them. Established mechanisms and processes are not adapted to the complexity and very short lifecycle of AI. Ongoing evaluation and monitoring mechanisms in the real-world context seem necessary, but the high degree of uncertainty associated with them requires a balance between the precautionary principle and a laissez-faire integration in clinical routine. Beyond the lack of clear frameworks and directives from the MSSS and other regulatory bodies regarding the use of these technologies by clinicians, inter-organisational networks facilitating the sharing of expertise and experience are essential. The current context is characterised by fragmentation, and poor communication and coordination between organisations and government agencies, which hinders an integrated and coherent vision of AI at the healthcare system: provincial- and federal-level of governance.

Contribution to the existing literature

The results of this study contribute to knowledge in several ways. They shed a new and different light on the trend of recent years where the literature has mainly focused on the technical and promissory dimensions of AI. Our findings are consistent with those of Pumplun et al. (2021) and Petersson et al. (2022) who analysed implementation issues raised by AI technologies in healthcare in Germany and Sweden, respectively [ 3 , 51 ]. Studies on telehealth and EHR also reported results that corroborate ours on AI [ 26 , 31 , 32 , 34 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. In this regard, several authors pointed out the major contrast between the techno-optimistic discourse on the performance and efficiency of technology and the reality of services that are difficult to transform [ 56 , 57 , 58 ]. These experiences have shown that the difficulties encountered in the deployment of digital technologies are mainly due to the historical lack of attention paid to the sociotechnical factors and conditions necessary for their integration into healthcare organisations and systems. Hence, our study adds to the growing literature that considers technology in a complex sociotechnical transformation perspective that requires not only technological but also human, clinical, professional, organisational, socio-political, economic, regulatory, legal, and cultural changes [ 27 , 40 , 41 , 56 , 59 , 60 , 61 ]. Very limited attention has been paid to this perspective in examining AI to date, whereas our study clarifies its contribution and indicates some avenues for future research (Table  4 ) [ 3 , 18 , 26 , 51 ].

From a theoretical standpoint, our study provides an original contribution to the literature on health innovations. It is one of the first to demonstrate that the NASSS framework is relevant for the analysis of the integration of AI technologies in healthcare organisations and systems [ 51 ]. The study contributes to the knowledge on the importance of a sociotechnical perspective to understand the complexity and unpredictability of transformations related to disruptive innovations such as AI [ 27 , 51 , 62 ].

Implication for practice and policy

Our study provides new insights for decision-making and practice on the conditions required but also on the pitfalls to be avoided to ensure successful integration of AI technologies into healthcare organisations and systems. It shows that the pitfalls of the technocentric vision of digital health of the last thirty years in Quebec (and elsewhere too) could easily be repeated with AI technologies, but this time with more profound repercussions [ 31 , 32 , 33 , 35 , 36 , 63 ]. As Matheny et al. (2020) highlighted: “Disconnects between reality and expectations have led to prior precipitous declines in use of the technology, termed AI winters, and another such event is possible, especially in health care” [ 64 ]. In this regard, the various stakeholders must be aware that AI is more an object of transformation at all levels of healthcare system governance, than a simple “intrinsically good/bad” tool. Its successful integration depends on several structural conditions, namely, appropriate: regulatory and governance frameworks; funding, business, and remuneration models; definition of the value proposition; management of conflicts of interest; governance of data; cybersecurity strategies; training and expertise, models of care and service delivery; inter-professional collaboration; and up-to-date infrastructure and equipment.

Specifically, AI highlights the importance of rethinking the collaboration between healthcare organisations and systems, on the one hand, and technology providers, on the other hand. Indeed, their interests sometimes represent competing financial and political objectives between which a difficult balance must be established [ 65 ]. Given their disruptive nature at all levels of the healthcare system, IA technologies could generate tensions and require trade-offs between perceptions, expectations, interests, and agendas that may be divergent or even antagonistic (ex. industry and venture capital, decision-makers, managers, clinicians, patients). These dynamics and power relations influence the trajectory of AI technologies in healthcare, either positively or negatively [ 59 , 66 ]. Thus, if healthcare organisations and systems are not sufficiently equipped and prepared, “the AI landscape risks being shaped by early established companies and decisions made with insufficient evaluations in place due to pressures to embrace technology” [ 67 ].

In addition, one of the fundamental issues remains the lack of digital literacy and culture, and AI technology skills among healthcare professionals [ 68 ]. Currently, initial and continuing training programmes do not sufficiently integrate these technologies into the expertise that trainees (e.g., physicians, nurses) need to achieve to be authorised to practice. As reported in our study, without appropriate training, clinicians are unlikely to adopt in an appropriate way these technologies. Indeed, training is required to adapt provider protocols, administrative workflows, pathways, and business processes [ 67 ]. According to Mistry (2019), for such change to take place, healthcare professionals will need:1) to have access to education content enabling them to learn new skills as AI users and work differently; 2) to be able to train AI systems themselves for setting them up to perform specified tasks, which implies knowing what data to select and its quality; 3) to develop abilities to interpret AI outputs, including a solid understanding of its limitations and bounds of function; and 4) to know “how the system learns and what constitutes appropriate use, so that ethical norms are upheld and any introduction of biases is avoided” [ 67 ].

Strengths and limitations

This study offers one of the first holistic and multilevel analyses of the complexity of the changes and transformations associated with the integration of AI technologies into clinical routine, beyond technical issues. It is also part of the few studies that go beyond looking at one single AI technology and delves into the organisational and systemic complexity of integrating multiple AI technologies concurrently.

However, the study has limitations. By its qualitative nature, it has a high level of internal validity, but the transferability (or generalisability) of its findings is limited to similar healthcare organisations and systems. In other contexts, it can increase the awareness of different stakeholders regarding the importance of taking better account of the sociotechnical dimension of AI. Healthcare organisations and systems can vary considerably, hence the importance of contextualising the results.

The number of interviewees ( n  = 29) is relatively low in view of the large number of AI technologies covered in this study. Although we made great efforts to include a wide range of stakeholders, several people were unable to participate due to the COVID-19 context. This is the case for women heading technology companies, whereas decision-makers, managers, and clinicians were unable to participate because of their direct involvement in the management of the pandemic. However, the people who participated, through their expertise and experience, provided us with rich data, necessary for a detailed understanding of the challenges of integrating AI in healthcare organisations and systems. The application of a rigorous research approach, guided by best methodological practices and an exhaustive theoretical framework, has reinforced the reliability of our results.

Conclusions

AI in healthcare is still in its infancy. There are huge expectations that it will provide answers to major contemporary challenges in healthcare organisations and systems. This is reflected in the funding it receives from governments, but also in the interest of the financial and venture capital sector. The COVID-19 pandemic was a test case for AI, and it did not fully deliver. However, the pandemic has served as an accelerator for its experimentation, for example, through the relaxation of regulatory requirements and less resistance from some stakeholders. AI represents as much a logistical, psychological, cultural, and philosophical change, particularly in terms of what it could and should do in healthcare organisations and systems. It is a “new era” that requires a real critical examination to learn from the many past experiences with the digitalisation of healthcare organisations and systems. With AI, the nature, scale and complexity of the changes and transformations are at such a level and intensity that the implications could be profound for society. At present, little is known about how such an announced revolution may take shape and under what conditions. This study provides a unique learning base for analysing AI technologies in healthcare organisations and systems from a sociotechnical perspective using the NASSS framework. It adds to the existing literature and can better inform decision-making towards the judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author (HA) upon reasonable request. The data are not publicly available due to information that could compromise the privacy of the research participants.

Abbreviations

• Artificial intelligence

Canadian Dollar

Coronavirus Disease 2019

Quebec’s Ministry of Health and Social Services Information Technology Division

Electronic Health Record

International Organization for Standardization

Information Technology

Act on Contracting by Public Bodies

Magnetic Resonance Imaging

Quebec’s Ministry of Health and Social Services

Non-Adoption, Abandonment, Scale-up, Spread, Sustainability

Picture Archiving and Communication System

Portable Document Format

Quality-Adjusted Life Year

Software as a Service

Software as a Medical Device

Start-ups and Small- and Medium-sized Enterprises

United States Dollar

United States of America

Value-Based Procurement

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Acknowledgements

We thank the interviewees and the City hospital personnel for their availability throughout the study, even in the midst of the COVID-19 pandemic. The findings and conclusions presented in the text are those of the authors. They do not necessarily reflect the position of their organisations.

HA was supported by the In Fieri research programme (led by P), the International Observatory on the Societal Impacts of Artificial Intelligence and Digital Technologies, and the Institute for Data Valorization (IVADO), (Canada).

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Hassane Alami

Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Hassane Alami, Chrysanthi Papoutsi & Sara E. Shaw

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HA and PL conceived and designed the study plan. HA and PL were responsible for data collection, analysis, and interpretation of results. HA, PL, CP, SES, RF and JPF were engaged in the drafting of the manuscript, and they all read and approved the final manuscript.

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The study was approved by the City hospital Research Ethics Committee (Number: Comité d’éthique de la recherche- City hospital: 20.399). (Address is anonymised for confidentiality reasons). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardian(s).

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Alami, H., Lehoux, P., Papoutsi, C. et al. Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre. BMC Health Serv Res 24 , 701 (2024). https://doi.org/10.1186/s12913-024-11112-x

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Received : 03 February 2023

Accepted : 14 May 2024

Published : 03 June 2024

DOI : https://doi.org/10.1186/s12913-024-11112-x

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