qualitative research to analysis

• Privacy Policy

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

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

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalisability

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

• Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

Cite this Scribbr article

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

Bhandari, P. (2023, January 30). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-qualitative-research/

Is this article helpful?

Pritha Bhandari

• Search Menu
• Browse content in Arts and Humanities
• Browse content in Archaeology
• Anglo-Saxon and Medieval Archaeology
• Archaeological Methodology and Techniques
• Archaeology by Region
• Archaeology of Religion
• Archaeology of Trade and Exchange
• Biblical Archaeology
• Contemporary and Public Archaeology
• Environmental Archaeology
• Historical Archaeology
• History and Theory of Archaeology
• Industrial Archaeology
• Landscape Archaeology
• Mortuary Archaeology
• Prehistoric Archaeology
• Underwater Archaeology
• Urban Archaeology
• Zooarchaeology
• Browse content in Architecture
• Architectural Structure and Design
• History of Architecture
• Residential and Domestic Buildings
• Theory of Architecture
• Browse content in Art
• Art Subjects and Themes
• History of Art
• Industrial and Commercial Art
• Theory of Art
• Biographical Studies
• Byzantine Studies
• Browse content in Classical Studies
• Classical Literature
• Classical Reception
• Classical History
• Classical Philosophy
• Classical Mythology
• Classical Art and Architecture
• Classical Oratory and Rhetoric
• Greek and Roman Papyrology
• Greek and Roman Archaeology
• Greek and Roman Epigraphy
• Greek and Roman Law
• Late Antiquity
• Religion in the Ancient World
• Digital Humanities
• Browse content in History
• Colonialism and Imperialism
• Diplomatic History
• Environmental History
• Genealogy, Heraldry, Names, and Honours
• Genocide and Ethnic Cleansing
• Historical Geography
• History by Period
• History of Emotions
• History of Agriculture
• History of Education
• History of Gender and Sexuality
• Industrial History
• Intellectual History
• International History
• Labour History
• Legal and Constitutional History
• Local and Family History
• Maritime History
• Military History
• National Liberation and Post-Colonialism
• Oral History
• Political History
• Public History
• Regional and National History
• Revolutions and Rebellions
• Slavery and Abolition of Slavery
• Social and Cultural History
• Theory, Methods, and Historiography
• Urban History
• World History
• Browse content in Language Teaching and Learning
• Language Learning (Specific Skills)
• Language Teaching Theory and Methods
• Browse content in Linguistics
• Applied Linguistics
• Cognitive Linguistics
• Computational Linguistics
• Forensic Linguistics
• Grammar, Syntax and Morphology
• Historical and Diachronic Linguistics
• History of English
• Language Evolution
• Language Reference
• Language Variation
• Language Families
• Language Acquisition
• Lexicography
• Linguistic Anthropology
• Linguistic Theories
• Linguistic Typology
• Phonetics and Phonology
• Psycholinguistics
• Sociolinguistics
• Translation and Interpretation
• Writing Systems
• Browse content in Literature
• Bibliography
• Children's Literature Studies
• Literary Studies (Romanticism)
• Literary Studies (American)
• Literary Studies (Modernism)
• Literary Studies (Asian)
• Literary Studies (European)
• Literary Studies (Eco-criticism)
• Literary Studies - World
• Literary Studies (1500 to 1800)
• Literary Studies (19th Century)
• Literary Studies (20th Century onwards)
• Literary Studies (African American Literature)
• Literary Studies (British and Irish)
• Literary Studies (Early and Medieval)
• Literary Studies (Fiction, Novelists, and Prose Writers)
• Literary Studies (Gender Studies)
• Literary Studies (Graphic Novels)
• Literary Studies (History of the Book)
• Literary Studies (Plays and Playwrights)
• Literary Studies (Poetry and Poets)
• Literary Studies (Postcolonial Literature)
• Literary Studies (Queer Studies)
• Literary Studies (Science Fiction)
• Literary Studies (Travel Literature)
• Literary Studies (War Literature)
• Literary Studies (Women's Writing)
• Literary Theory and Cultural Studies
• Mythology and Folklore
• Shakespeare Studies and Criticism
• Browse content in Media Studies
• Browse content in Music
• Applied Music
• Dance and Music
• Ethics in Music
• Ethnomusicology
• Gender and Sexuality in Music
• Medicine and Music
• Music Cultures
• Music and Media
• Music and Culture
• Music and Religion
• Music Education and Pedagogy
• Music Theory and Analysis
• Musical Scores, Lyrics, and Libretti
• Musical Structures, Styles, and Techniques
• Musicology and Music History
• Performance Practice and Studies
• Race and Ethnicity in Music
• Sound Studies
• Browse content in Performing Arts
• Browse content in Philosophy
• Aesthetics and Philosophy of Art
• Epistemology
• Feminist Philosophy
• History of Western Philosophy
• Metaphysics
• Moral Philosophy
• Non-Western Philosophy
• Philosophy of Language
• Philosophy of Mind
• Philosophy of Perception
• Philosophy of Action
• Philosophy of Law
• Philosophy of Religion
• Philosophy of Science
• Philosophy of Mathematics and Logic
• Practical Ethics
• Social and Political Philosophy
• Browse content in Religion
• Biblical Studies
• Christianity
• East Asian Religions
• History of Religion
• Judaism and Jewish Studies
• Qumran Studies
• Religion and Education
• Religion and Health
• Religion and Politics
• Religion and Science
• Religion and Law
• Religion and Art, Literature, and Music
• Religious Studies
• Browse content in Society and Culture
• Cookery, Food, and Drink
• Cultural Studies
• Customs and Traditions
• Ethical Issues and Debates
• Hobbies, Games, Arts and Crafts
• Lifestyle, Home, and Garden
• Natural world, Country Life, and Pets
• Popular Beliefs and Controversial Knowledge
• Sports and Outdoor Recreation
• Technology and Society
• Travel and Holiday
• Visual Culture
• Browse content in Law
• Arbitration
• Browse content in Company and Commercial Law
• Commercial Law
• Company Law
• Browse content in Comparative Law
• Systems of Law
• Competition Law
• Browse content in Constitutional and Administrative Law
• Government Powers
• Judicial Review
• Local Government Law
• Military and Defence Law
• Parliamentary and Legislative Practice
• Construction Law
• Contract Law
• Browse content in Criminal Law
• Criminal Procedure
• Criminal Evidence Law
• Sentencing and Punishment
• Employment and Labour Law
• Environment and Energy Law
• Browse content in Financial Law
• Banking Law
• Insolvency Law
• History of Law
• Human Rights and Immigration
• Intellectual Property Law
• Browse content in International Law
• Private International Law and Conflict of Laws
• Public International Law
• IT and Communications Law
• Jurisprudence and Philosophy of Law
• Law and Society
• Law and Politics
• Browse content in Legal System and Practice
• Courts and Procedure
• Legal Skills and Practice
• Primary Sources of Law
• Regulation of Legal Profession
• Medical and Healthcare Law
• Browse content in Policing
• Criminal Investigation and Detection
• Police and Security Services
• Police Procedure and Law
• Police Regional Planning
• Browse content in Property Law
• Personal Property Law
• Study and Revision
• Terrorism and National Security Law
• Browse content in Trusts Law
• Wills and Probate or Succession
• Browse content in Medicine and Health
• Browse content in Allied Health Professions
• Arts Therapies
• Clinical Science
• Dietetics and Nutrition
• Occupational Therapy
• Operating Department Practice
• Physiotherapy
• Radiography
• Speech and Language Therapy
• Browse content in Anaesthetics
• General Anaesthesia
• Neuroanaesthesia
• Clinical Neuroscience
• Browse content in Clinical Medicine
• Acute Medicine
• Cardiovascular Medicine
• Clinical Genetics
• Clinical Pharmacology and Therapeutics
• Dermatology
• Endocrinology and Diabetes
• Gastroenterology
• Genito-urinary Medicine
• Geriatric Medicine
• Infectious Diseases
• Medical Toxicology
• Medical Oncology
• Pain Medicine
• Palliative Medicine
• Rehabilitation Medicine
• Respiratory Medicine and Pulmonology
• Rheumatology
• Sleep Medicine
• Sports and Exercise Medicine
• Community Medical Services
• Critical Care
• Emergency Medicine
• Forensic Medicine
• Haematology
• History of Medicine
• Browse content in Medical Skills
• Clinical Skills
• Communication Skills
• Nursing Skills
• Surgical Skills
• Medical Ethics
• Browse content in Medical Dentistry
• Oral and Maxillofacial Surgery
• Paediatric Dentistry
• Restorative Dentistry and Orthodontics
• Surgical Dentistry
• Medical Statistics and Methodology
• Browse content in Neurology
• Clinical Neurophysiology
• Neuropathology
• Nursing Studies
• Browse content in Obstetrics and Gynaecology
• Gynaecology
• Occupational Medicine
• Ophthalmology
• Otolaryngology (ENT)
• Browse content in Paediatrics
• Neonatology
• Browse content in Pathology
• Chemical Pathology
• Clinical Cytogenetics and Molecular Genetics
• Histopathology
• Medical Microbiology and Virology
• Patient Education and Information
• Browse content in Pharmacology
• Psychopharmacology
• Browse content in Popular Health
• Caring for Others
• Complementary and Alternative Medicine
• Self-help and Personal Development
• Browse content in Preclinical Medicine
• Cell Biology
• Molecular Biology and Genetics
• Reproduction, Growth and Development
• Primary Care
• Professional Development in Medicine
• Browse content in Psychiatry
• Addiction Medicine
• Child and Adolescent Psychiatry
• Forensic Psychiatry
• Learning Disabilities
• Old Age Psychiatry
• Psychotherapy
• Browse content in Public Health and Epidemiology
• Epidemiology
• Public Health
• Browse content in Radiology
• Clinical Radiology
• Interventional Radiology
• Nuclear Medicine
• Radiation Oncology
• Reproductive Medicine
• Browse content in Surgery
• Cardiothoracic Surgery
• Gastro-intestinal and Colorectal Surgery
• General Surgery
• Neurosurgery
• Paediatric Surgery
• Peri-operative Care
• Plastic and Reconstructive Surgery
• Surgical Oncology
• Transplant Surgery
• Trauma and Orthopaedic Surgery
• Vascular Surgery
• Browse content in Science and Mathematics
• Browse content in Biological Sciences
• Aquatic Biology
• Biochemistry
• Bioinformatics and Computational Biology
• Developmental Biology
• Ecology and Conservation
• Evolutionary Biology
• Genetics and Genomics
• Microbiology
• Molecular and Cell Biology
• Natural History
• Plant Sciences and Forestry
• Research Methods in Life Sciences
• Structural Biology
• Systems Biology
• Zoology and Animal Sciences
• Browse content in Chemistry
• Analytical Chemistry
• Computational Chemistry
• Crystallography
• Environmental Chemistry
• Industrial Chemistry
• Inorganic Chemistry
• Materials Chemistry
• Medicinal Chemistry
• Mineralogy and Gems
• Organic Chemistry
• Physical Chemistry
• Polymer Chemistry
• Study and Communication Skills in Chemistry
• Theoretical Chemistry
• Browse content in Computer Science
• Artificial Intelligence
• Computer Architecture and Logic Design
• Game Studies
• Human-Computer Interaction
• Mathematical Theory of Computation
• Programming Languages
• Software Engineering
• Systems Analysis and Design
• Virtual Reality
• Browse content in Computing
• Business Applications
• Computer Games
• Computer Security
• Computer Networking and Communications
• Digital Lifestyle
• Graphical and Digital Media Applications
• Operating Systems
• Browse content in Earth Sciences and Geography
• Atmospheric Sciences
• Environmental Geography
• Geology and the Lithosphere
• Maps and Map-making
• Meteorology and Climatology
• Oceanography and Hydrology
• Palaeontology
• Physical Geography and Topography
• Regional Geography
• Soil Science
• Urban Geography
• Browse content in Engineering and Technology
• Agriculture and Farming
• Biological Engineering
• Civil Engineering, Surveying, and Building
• Electronics and Communications Engineering
• Energy Technology
• Engineering (General)
• Environmental Science, Engineering, and Technology
• History of Engineering and Technology
• Mechanical Engineering and Materials
• Technology of Industrial Chemistry
• Transport Technology and Trades
• Browse content in Environmental Science
• Applied Ecology (Environmental Science)
• Conservation of the Environment (Environmental Science)
• Environmental Sustainability
• Environmentalist Thought and Ideology (Environmental Science)
• Management of Land and Natural Resources (Environmental Science)
• Natural Disasters (Environmental Science)
• Nuclear Issues (Environmental Science)
• Pollution and Threats to the Environment (Environmental Science)
• Social Impact of Environmental Issues (Environmental Science)
• History of Science and Technology
• Browse content in Materials Science
• Ceramics and Glasses
• Composite Materials
• Metals, Alloying, and Corrosion
• Nanotechnology
• Browse content in Mathematics
• Applied Mathematics
• Biomathematics and Statistics
• History of Mathematics
• Mathematical Education
• Mathematical Finance
• Mathematical Analysis
• Numerical and Computational Mathematics
• Probability and Statistics
• Pure Mathematics
• Browse content in Neuroscience
• Cognition and Behavioural Neuroscience
• Development of the Nervous System
• Disorders of the Nervous System
• History of Neuroscience
• Invertebrate Neurobiology
• Molecular and Cellular Systems
• Neuroendocrinology and Autonomic Nervous System
• Neuroscientific Techniques
• Sensory and Motor Systems
• Browse content in Physics
• Astronomy and Astrophysics
• Atomic, Molecular, and Optical Physics
• Biological and Medical Physics
• Classical Mechanics
• Computational Physics
• Condensed Matter Physics
• Electromagnetism, Optics, and Acoustics
• History of Physics
• Mathematical and Statistical Physics
• Measurement Science
• Nuclear Physics
• Particles and Fields
• Plasma Physics
• Quantum Physics
• Relativity and Gravitation
• Semiconductor and Mesoscopic Physics
• Browse content in Psychology
• Affective Sciences
• Clinical Psychology
• Cognitive Psychology
• Cognitive Neuroscience
• Criminal and Forensic Psychology
• Developmental Psychology
• Educational Psychology
• Evolutionary Psychology
• Health Psychology
• History and Systems in Psychology
• Music Psychology
• Neuropsychology
• Organizational Psychology
• Psychological Assessment and Testing
• Psychology of Human-Technology Interaction
• Psychology Professional Development and Training
• Research Methods in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Human Evolution
• Medical Anthropology
• Physical Anthropology
• Regional Anthropology
• Social and Cultural Anthropology
• Theory and Practice of Anthropology
• Browse content in Business and Management
• Business Ethics
• Business History
• Business Strategy
• Business and Technology
• Business and Government
• Business and the Environment
• Comparative Management
• Corporate Governance
• Corporate Social Responsibility
• Entrepreneurship
• Health Management
• Human Resource Management
• Industrial and Employment Relations
• Industry Studies
• Information and Communication Technologies
• International Business
• Knowledge Management
• Management and Management Techniques
• Operations Management
• Organizational Theory and Behaviour
• Pensions and Pension Management
• Public and Nonprofit Management
• Strategic Management
• Supply Chain Management
• Browse content in Criminology and Criminal Justice
• Criminal Justice
• Criminology
• Forms of Crime
• International and Comparative Criminology
• Youth Violence and Juvenile Justice
• Development Studies
• Browse content in Economics
• Agricultural, Environmental, and Natural Resource Economics
• Asian Economics
• Behavioural Finance
• Behavioural Economics and Neuroeconomics
• Econometrics and Mathematical Economics
• Economic History
• Economic Methodology
• Economic Systems
• Economic Development and Growth
• Financial Markets
• Financial Institutions and Services
• General Economics and Teaching
• Health, Education, and Welfare
• History of Economic Thought
• International Economics
• Labour and Demographic Economics
• Law and Economics
• Macroeconomics and Monetary Economics
• Microeconomics
• Public Economics
• Urban, Rural, and Regional Economics
• Welfare Economics
• Browse content in Education
• Adult Education and Continuous Learning
• Care and Counselling of Students
• Early Childhood and Elementary Education
• Educational Equipment and Technology
• Educational Strategies and Policy
• Higher and Further Education
• Organization and Management of Education
• Philosophy and Theory of Education
• Schools Studies
• Secondary Education
• Teaching of a Specific Subject
• Teaching of Specific Groups and Special Educational Needs
• Teaching Skills and Techniques
• Browse content in Environment
• Applied Ecology (Social Science)
• Climate Change
• Conservation of the Environment (Social Science)
• Environmentalist Thought and Ideology (Social Science)
• Natural Disasters (Environment)
• Social Impact of Environmental Issues (Social Science)
• Browse content in Human Geography
• Cultural Geography
• Economic Geography
• Political Geography
• Browse content in Interdisciplinary Studies
• Communication Studies
• Museums, Libraries, and Information Sciences
• Browse content in Politics
• African Politics
• Asian Politics
• Chinese Politics
• Comparative Politics
• Conflict Politics
• Elections and Electoral Studies
• Environmental Politics
• European Union
• Foreign Policy
• Gender and Politics
• Human Rights and Politics
• Indian Politics
• International Relations
• International Organization (Politics)
• International Political Economy
• Irish Politics
• Latin American Politics
• Middle Eastern Politics
• Political Behaviour
• Political Economy
• Political Institutions
• Political Theory
• Political Methodology
• Political Communication
• Political Philosophy
• Political Sociology
• Politics and Law
• Public Policy
• Public Administration
• Quantitative Political Methodology
• Regional Political Studies
• Russian Politics
• Security Studies
• State and Local Government
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• African Studies
• Asian Studies
• East Asian Studies
• Japanese Studies
• Latin American Studies
• Middle Eastern Studies
• Native American Studies
• Scottish Studies
• Browse content in Research and Information
• Research Methods
• Browse content in Social Work
• Addictions and Substance Misuse
• Adoption and Fostering
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Developmental and Physical Disabilities Social Work
• Direct Practice and Clinical Social Work
• Emergency Services
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Sport and Leisure
• Urban and Rural Studies
• Browse content in Warfare and Defence
• Defence Strategy, Planning, and Research
• Land Forces and Warfare
• Military Administration
• Military Life and Institutions
• Naval Forces and Warfare
• Other Warfare and Defence Issues
• Peace Studies and Conflict Resolution
• Weapons and Equipment

The Oxford Handbook of Qualitative Research

Patricia Leavy Independent Scholar Kennebunk, ME, USA

A newer edition of this book is available.

• Cite Icon Cite
• Permissions Icon Permissions

This handbook provides a broad introduction to qualitative research to those with little to no background in the subject while simultaneously providing substantive contributions to the field that will be of interest to even the most experienced researchers. The first two sections explore the history of qualitative research, ethical perspectives, and philosophical/theoretical approaches. The next three sections focus on the major methods of qualitative practice, as well as on newer approaches (such as arts-based research and internet research); area studies often excluded (such as museum studies and disaster studies); and mixed methods and participatory methods (such as community-based research). The next section covers key issues including data analysis, interpretation, writing, and assessment. The final section offers a commentary about politics and research and the move toward public scholarship. The Oxford Handbook of Qualitative Research is intended for students of all levels, faculty, and researchers across the social sciences.

Signed in as

Institutional accounts.

• GoogleCrawler [DO NOT DELETE]
• Google Scholar Indexing

Personal account

• Sign in with email/username & password
• Get email alerts
• Save searches
• Purchase content
• Activate your purchase/trial code
• Add your ORCID iD

Institutional access

Sign in with a library card.

• Sign in with username/password
• Recommend to your librarian
• Institutional account management
• Get help with access

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:

IP based access

Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.

Sign in through your institution

Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.

• Click Sign in through your institution.
• Select your institution from the list provided, which will take you to your institution's website to sign in.
• When on the institution site, please use the credentials provided by your institution. Do not use an Oxford Academic personal account.
• Following successful sign in, you will be returned to Oxford Academic.

If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.

Enter your library card number to sign in. If you cannot sign in, please contact your librarian.

Society Members

Society member access to a journal is achieved in one of the following ways:

Sign in through society site

Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:

• Click Sign in through society site.
• When on the society site, please use the credentials provided by that society. Do not use an Oxford Academic personal account.

If you do not have a society account or have forgotten your username or password, please contact your society.

Sign in using a personal account

Some societies use Oxford Academic personal accounts to provide access to their members. See below.

A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.

Some societies use Oxford Academic personal accounts to provide access to their members.

Viewing your signed in accounts

Click the account icon in the top right to:

• View your signed in personal account and access account management features.
• View the institutional accounts that are providing access.

Signed in but can't access content

Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.

For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.

Our books are available by subscription or purchase to libraries and institutions.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Rights and permissions
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

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).   

Events and Workshops

• Introduction to NVivo Have you just collected your data and wondered what to do next? Come join us for an introductory session on utilizing NVivo to support your analytical process. This session will only cover features of the software and how to import your records. Please feel free to attend any of the following sessions below: April 25th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125 May 9th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125 May 30th, 2024 12:30 pm - 1:45 pm Green Library - SVA Conference Room 125
• Next: Choose an approach >>
• Choose an approach
• Find studies
• Learn methods
• Get software
• Get data for secondary analysis
• Network with researchers

• Last Updated: Apr 2, 2024 10:41 AM
• URL: https://guides.library.stanford.edu/qualitative_research

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

84 Comments

This has been very helpful. Thank you.

Thank you madam,

Thank you so much for this information

I wonder it so clear for understand and good for me. can I ask additional query?

Very insightful and useful

Good work done with clear explanations. Thank you.

Thanks so much for the write-up, it’s really good.

Thanks madam . It is very important .

thank you very good

This has been very well explained in simple language . It is useful even for a new researcher.

Great to hear that. Good luck with your qualitative data analysis, Pramod!

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.

Thank you so much.

very informative sequential presentation

Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!

Thank you very much, this is well explained and useful

i need a citation of your book.

Thanks a lot , remarkable indeed, enlighting to the best

Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

Keep writing useful artikel.

It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Thank you, this is well explained and very useful.

Very helpful .Thanks.

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

The session was very helpful and insightful. Thank you

This was very helpful and insightful. Easy to read and understand

As a professional academic writer, this has been so informative and educative. Keep up the good work Grad Coach you are unmatched with quality content for sure.

Keep up the good work Grad Coach you are unmatched with quality content for sure.

Its Great and help me the most. A Million Thanks you Dr.

It is a very nice work

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

This is Amazing and well explained, thanks

great overview

What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Very helpful indeed. Thanku so much for the insight.

This was incredibly helpful.

Very helpful.

very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

This was helpful. Thank you

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Data Information vs Insight: Essential differences

May 14, 2024

Pricing Analytics Software: Optimize Your Pricing Strategy

May 13, 2024

Relationship Marketing: What It Is, Examples & Top 7 Benefits

May 8, 2024

The Best Email Survey Tool to Boost Your Feedback Game

May 7, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

• AI & NLP
• Churn & Loyalty
• Customer Experience
• Customer Journeys
• Customer Metrics
• Feedback Analysis
• Product Experience
• Product Updates
• Sentiment Analysis
• Surveys & Feedback Collection
• Try Thematic

Welcome to the community

Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

Community & Marketing

Tyler manages our community of CX, insights & analytics professionals. Tyler's goal is to help unite insights professionals around common challenges.

We make it easy to discover the customer and product issues that matter.

Unlock the value of feedback at scale, in one platform. Try it for free now!

• Questions to ask your Feedback Analytics vendor
• How to end customer churn for good
• Scalable analysis of NPS verbatims
• 5 Text analytics approaches
• How to calculate the ROI of CX

Our experts will show you how Thematic works, how to discover pain points and track the ROI of decisions. To access your free trial, book a personal demo today.

Recent posts

Watercare is New Zealand's largest water and wastewater service provider. They are responsible for bringing clean water to 1.7 million people in Tamaki Makaurau (Auckland) and safeguarding the wastewater network to minimize impact on the environment. Water is a sector that often gets taken for granted, with drainage and

Become a qualitative theming pro! Creating a perfect code frame is hard, but thematic analysis software makes the process much easier.

Qualtrics is one of the most well-known and powerful Customer Feedback Management platforms. But even so, it has limitations. We recently hosted a live panel where data analysts from two well-known brands shared their experiences with Qualtrics, and how they extended this platform’s capabilities. Below, we’ll share the

• 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

728k Accesses

294 Citations

85 Altmetric

Metrics details

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

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

What is qualitative research?

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

Why conduct qualitative research?

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

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

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

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

Iterative research process

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

Data collection

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

Document study

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

Observations

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

Semi-structured interviews

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

Focus groups

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

Choosing the “right” method

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

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

Possible combination of data collection methods

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

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

Data analysis

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

From data collection to data analysis

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

How to report qualitative research?

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

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

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

How to assess qualitative research?

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

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

Reflexivity

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

Sampling and saturation

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

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

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

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

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

Member checking

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

Stakeholder involvement

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

How not to assess qualitative research

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

Protocol adherence

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

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

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

Interrater reliability, variability and other “objectivity checks”

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

Not being quantitative research

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

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

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

Philipsen, H., & Vernooij-Dassen, M. (2007). Kwalitatief onderzoek: nuttig, onmisbaar en uitdagend. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Qualitative research: useful, indispensable and challenging. In: Qualitative research: Practical methods for medical practice (pp. 5–12). Houten: Bohn Stafleu van Loghum.

Chapter   Google Scholar  

Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches . London: Sage.

Kelly, J., Dwyer, J., Willis, E., & Pekarsky, B. (2014). Travelling to the city for hospital care: Access factors in country aboriginal patient journeys. Australian Journal of Rural Health, 22 (3), 109–113.

Article   Google Scholar  

Nilsen, P., Ståhl, C., Roback, K., & Cairney, P. (2013). Never the twain shall meet? - a comparison of implementation science and policy implementation research. Implementation Science, 8 (1), 1–12.

Howick J, Chalmers I, Glasziou, P., Greenhalgh, T., Heneghan, C., Liberati, A., Moschetti, I., Phillips, B., & Thornton, H. (2011). The 2011 Oxford CEBM evidence levels of evidence (introductory document) . Oxford Center for Evidence Based Medicine. https://www.cebm.net/2011/06/2011-oxford-cebm-levels-evidence-introductory-document/ .

Eakin, J. M. (2016). Educating critical qualitative health researchers in the land of the randomized controlled trial. Qualitative Inquiry, 22 (2), 107–118.

May, A., & Mathijssen, J. (2015). Alternatieven voor RCT bij de evaluatie van effectiviteit van interventies!? Eindrapportage. In Alternatives for RCTs in the evaluation of effectiveness of interventions!? Final report .

Google Scholar  

Berwick, D. M. (2008). The science of improvement. Journal of the American Medical Association, 299 (10), 1182–1184.

Article   CAS   Google Scholar  

Christ, T. W. (2014). Scientific-based research and randomized controlled trials, the “gold” standard? Alternative paradigms and mixed methodologies. Qualitative Inquiry, 20 (1), 72–80.

Lamont, T., Barber, N., Jd, P., Fulop, N., Garfield-Birkbeck, S., Lilford, R., Mear, L., Raine, R., & Fitzpatrick, R. (2016). New approaches to evaluating complex health and care systems. BMJ, 352:i154.

Drabble, S. J., & O’Cathain, A. (2015). Moving from Randomized Controlled Trials to Mixed Methods Intervention Evaluation. In S. Hesse-Biber & R. B. Johnson (Eds.), The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (pp. 406–425). London: Oxford University Press.

Chambers, D. A., Glasgow, R. E., & Stange, K. C. (2013). The dynamic sustainability framework: Addressing the paradox of sustainment amid ongoing change. Implementation Science : IS, 8 , 117.

Hak, T. (2007). Waarnemingsmethoden in kwalitatief onderzoek. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Observation methods in qualitative research] (pp. 13–25). Houten: Bohn Stafleu van Loghum.

Russell, C. K., & Gregory, D. M. (2003). Evaluation of qualitative research studies. Evidence Based Nursing, 6 (2), 36–40.

Fossey, E., Harvey, C., McDermott, F., & Davidson, L. (2002). Understanding and evaluating qualitative research. Australian and New Zealand Journal of Psychiatry, 36 , 717–732.

Yanow, D. (2000). Conducting interpretive policy analysis (Vol. 47). Thousand Oaks: Sage University Papers Series on Qualitative Research Methods.

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for Information, 22 , 63–75.

van der Geest, S. (2006). Participeren in ziekte en zorg: meer over kwalitatief onderzoek. Huisarts en Wetenschap, 49 (4), 283–287.

Hijmans, E., & Kuyper, M. (2007). Het halfopen interview als onderzoeksmethode. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [The half-open interview as research method (pp. 43–51). Houten: Bohn Stafleu van Loghum.

Jansen, H. (2007). Systematiek en toepassing van de kwalitatieve survey. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Systematics and implementation of the qualitative survey (pp. 27–41). Houten: Bohn Stafleu van Loghum.

Pv, R., & Peremans, L. (2007). Exploreren met focusgroepgesprekken: de ‘stem’ van de groep onder de loep. In L. PLBJ & H. TCo (Eds.), Kwalitatief onderzoek: Praktische methoden voor de medische praktijk . [Exploring with focus group conversations: the “voice” of the group under the magnifying glass (pp. 53–64). Houten: Bohn Stafleu van Loghum.

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology Nursing Forum, 41 (5), 545–547.

Boeije H: Analyseren in kwalitatief onderzoek: Denken en doen, [Analysis in qualitative research: Thinking and doing] vol. Den Haag Boom Lemma uitgevers; 2012.

Hunter, A., & Brewer, J. (2015). Designing Multimethod Research. In S. Hesse-Biber & R. B. Johnson (Eds.), The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (pp. 185–205). London: Oxford University Press.

Archibald, M. M., Radil, A. I., Zhang, X., & Hanson, W. E. (2015). Current mixed methods practices in qualitative research: A content analysis of leading journals. International Journal of Qualitative Methods, 14 (2), 5–33.

Creswell, J. W., & Plano Clark, V. L. (2011). Choosing a Mixed Methods Design. In Designing and Conducting Mixed Methods Research . Thousand Oaks: SAGE Publications.

Mays, N., & Pope, C. (2000). Assessing quality in qualitative research. BMJ, 320 (7226), 50–52.

O'Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine : Journal of the Association of American Medical Colleges, 89 (9), 1245–1251.

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality and Quantity, 52 (4), 1893–1907.

Moser, A., & Korstjens, I. (2018). Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. European Journal of General Practice, 24 (1), 9–18.

Marlett, N., Shklarov, S., Marshall, D., Santana, M. J., & Wasylak, T. (2015). Building new roles and relationships in research: A model of patient engagement research. Quality of Life Research : an international journal of quality of life aspects of treatment, care and rehabilitation, 24 (5), 1057–1067.

Demian, M. N., Lam, N. N., Mac-Way, F., Sapir-Pichhadze, R., & Fernandez, N. (2017). Opportunities for engaging patients in kidney research. Canadian Journal of Kidney Health and Disease, 4 , 2054358117703070–2054358117703070.

Noyes, J., McLaughlin, L., Morgan, K., Roberts, A., Stephens, M., Bourne, J., Houlston, M., Houlston, J., Thomas, S., Rhys, R. G., et al. (2019). Designing a co-productive study to overcome known methodological challenges in organ donation research with bereaved family members. Health Expectations . 22(4):824–35.

Piil, K., Jarden, M., & Pii, K. H. (2019). Research agenda for life-threatening cancer. European Journal Cancer Care (Engl), 28 (1), e12935.

Hofmann, D., Ibrahim, F., Rose, D., Scott, D. L., Cope, A., Wykes, T., & Lempp, H. (2015). Expectations of new treatment in rheumatoid arthritis: Developing a patient-generated questionnaire. Health Expectations : an international journal of public participation in health care and health policy, 18 (5), 995–1008.

Jun, M., Manns, B., Laupacis, A., Manns, L., Rehal, B., Crowe, S., & Hemmelgarn, B. R. (2015). Assessing the extent to which current clinical research is consistent with patient priorities: A scoping review using a case study in patients on or nearing dialysis. Canadian Journal of Kidney Health and Disease, 2 , 35.

Elsie Baker, S., & Edwards, R. (2012). How many qualitative interviews is enough? In National Centre for Research Methods Review Paper . National Centre for Research Methods. http://eprints.ncrm.ac.uk/2273/4/how_many_interviews.pdf .

Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18 (2), 179–183.

Sim, J., Saunders, B., Waterfield, J., & Kingstone, T. (2018). Can sample size in qualitative research be determined a priori? International Journal of Social Research Methodology, 21 (5), 619–634.

Download references

Acknowledgements

no external funding.

Author information

Authors and affiliations.

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

Loraine Busetto, Wolfgang Wick & Christoph Gumbinger

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

Wolfgang Wick

You can also search for this author in PubMed   Google Scholar

Contributions

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

Corresponding author

Correspondence to Loraine Busetto .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 30 January 2020

Accepted : 22 April 2020

Published : 27 May 2020

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

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Qualitative research
• Mixed methods
• Quality assessment

Neurological Research and Practice

ISSN: 2524-3489

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence

Market Research

• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Qualitative Research

Try Qualtrics for free

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.

Ready to learn more about Qualtrics?

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

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.

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.

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.

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

• Åkerström M. Curiosity and serendipity in qualitative research. Qualitative Sociology Review. 2013; 9 (2):10–18. [ Google Scholar ]
• Alford, Robert R. 1998. The craft of inquiry. Theories, methods, evidence . Oxford: Oxford University Press.
• Alvesson M, Kärreman D. Qualitative research and theory development . Mystery as method . London: SAGE Publications; 2011. [ Google Scholar ]
• Aspers, Patrik. 2006. Markets in Fashion, A Phenomenological Approach. London Routledge.
• Atkinson P. Qualitative research. Unity and diversity. Forum: Qualitative Social Research. 2005; 6 (3):1–15. [ Google Scholar ]
• Becker HS. Outsiders. Studies in the sociology of deviance . New York: The Free Press; 1963. [ Google Scholar ]
• Becker HS. Whose side are we on? Social Problems. 1966; 14 (3):239–247. [ Google Scholar ]
• Becker HS. Sociological work. Method and substance. New Brunswick: Transaction Books; 1970. [ Google Scholar ]
• Becker HS. The epistemology of qualitative research. In: Richard J, Anne C, Shweder RA, editors. Ethnography and human development. Context and meaning in social inquiry. Chicago: University of Chicago Press; 1996. pp. 53–71. [ Google Scholar ]
• Becker HS. Tricks of the trade. How to think about your research while you're doing it. Chicago: University of Chicago Press; 1998. [ Google Scholar ]
• Becker, Howard S. 2017. Evidence . Chigaco: University of Chicago Press.
• Becker H, Geer B, Hughes E, Strauss A. Boys in White, student culture in medical school. New Brunswick: Transaction Publishers; 1961. [ Google Scholar ]
• Berezin M. How do we know what we mean? Epistemological dilemmas in cultural sociology. Qualitative Sociology. 2014; 37 (2):141–151. [ Google Scholar ]
• Best, Joel. 2004. Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , eds . Charles, Ragin, Joanne, Nagel, and Patricia White, 53-54. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf .
• Biernacki R. Humanist interpretation versus coding text samples. Qualitative Sociology. 2014; 37 (2):173–188. [ Google Scholar ]
• Blumer H. Symbolic interactionism: Perspective and method. Berkeley: University of California Press; 1969. [ Google Scholar ]
• Brady H, Collier D, Seawright J. Refocusing the discussion of methodology. In: Henry B, David C, editors. Rethinking social inquiry. Diverse tools, shared standards. Lanham: Rowman and Littlefield; 2004. pp. 3–22. [ Google Scholar ]
• Brown AP. Qualitative method and compromise in applied social research. Qualitative Research. 2010; 10 (2):229–248. [ Google Scholar ]
• Charmaz K. Constructing grounded theory. London: Sage; 2006. [ Google Scholar ]
• Corte, Ugo, and Katherine Irwin. 2017. “The Form and Flow of Teaching Ethnographic Knowledge: Hands-on Approaches for Learning Epistemology” Teaching Sociology 45(3): 209-219.
• Creswell JW. Research design. Qualitative, quantitative, and mixed method approaches. 3. Thousand Oaks: SAGE Publications; 2009. [ Google Scholar ]
• Davidsson D. The myth of the subjective. In: Davidsson D, editor. Subjective, intersubjective, objective. Oxford: Oxford University Press; 1988. pp. 39–52. [ Google Scholar ]
• Denzin NK. The research act: A theoretical introduction to Ssociological methods. Chicago: Aldine Publishing Company Publishers; 1970. [ Google Scholar ]
• Denzin NK, Lincoln YS. Introduction. The discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. Collecting and interpreting qualitative materials. Thousand Oaks: SAGE Publications; 2003. pp. 1–45. [ Google Scholar ]
• Denzin NK, Lincoln YS. Introduction. The discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. The Sage handbook of qualitative research. Thousand Oaks: SAGE Publications; 2005. pp. 1–32. [ Google Scholar ]
• Emerson RM, editor. Contemporary field research. A collection of readings. Prospect Heights: Waveland Press; 1988. [ Google Scholar ]
• Emerson RM, Fretz RI, Shaw LL. Writing ethnographic fieldnotes. Chicago: University of Chicago Press; 1995. [ Google Scholar ]
• Esterberg KG. Qualitative methods in social research. Boston: McGraw-Hill; 2002. [ Google Scholar ]
• Fine, Gary Alan. 1995. Review of “handbook of qualitative research.” Contemporary Sociology 24 (3): 416–418.
• Fine, Gary Alan. 2003. “ Toward a Peopled Ethnography: Developing Theory from Group Life.” Ethnography . 4(1):41-60.
• Fine GA, Hancock BH. The new ethnographer at work. Qualitative Research. 2017; 17 (2):260–268. [ Google Scholar ]
• Fine GA, Hallett T. Stranger and stranger: Creating theory through ethnographic distance and authority. Journal of Organizational Ethnography. 2014; 3 (2):188–203. [ Google Scholar ]
• Flick U. Qualitative research. State of the art. Social Science Information. 2002; 41 (1):5–24. [ Google Scholar ]
• Flick U. Designing qualitative research. London: SAGE Publications; 2007. [ Google Scholar ]
• Frankfort-Nachmias C, Nachmias D. Research methods in the social sciences. 5. London: Edward Arnold; 1996. [ Google Scholar ]
• Franzosi R. Sociology, narrative, and the quality versus quantity debate (Goethe versus Newton): Can computer-assisted story grammars help us understand the rise of Italian fascism (1919- 1922)? Theory and Society. 2010; 39 (6):593–629. [ Google Scholar ]
• Franzosi R. From method and measurement to narrative and number. International journal of social research methodology. 2016; 19 (1):137–141. [ Google Scholar ]
• Gadamer, Hans-Georg. 1990. Wahrheit und Methode, Grundzüge einer philosophischen Hermeneutik . Band 1, Hermeneutik. Tübingen: J.C.B. Mohr.
• Gans H. Participant Observation in an Age of “Ethnography” Journal of Contemporary Ethnography. 1999; 28 (5):540–548. [ Google Scholar ]
• Geertz C. The interpretation of cultures. New York: Basic Books; 1973. [ Google Scholar ]
• Gilbert N. Researching social life. 3. London: SAGE Publications; 2009. [ Google Scholar ]
• Glaeser A. Hermeneutic institutionalism: Towards a new synthesis. Qualitative Sociology. 2014; 37 :207–241. [ Google Scholar ]
• Glaser, Barney G., and Anselm L. Strauss. [1967] 2010. The discovery of grounded theory. Strategies for qualitative research. Hawthorne: Aldine.
• Goertz G, Mahoney J. A tale of two cultures: Qualitative and quantitative research in the social sciences. Princeton: Princeton University Press; 2012. [ Google Scholar ]
• Goffman E. On fieldwork. Journal of Contemporary Ethnography. 1989; 18 (2):123–132. [ Google Scholar ]
• Goodwin J, Horowitz R. Introduction. The methodological strengths and dilemmas of qualitative sociology. Qualitative Sociology. 2002; 25 (1):33–47. [ Google Scholar ]
• Habermas, Jürgen. [1981] 1987. The theory of communicative action . Oxford: Polity Press.
• Hammersley M. The issue of quality in qualitative research. International Journal of Research & Method in Education. 2007; 30 (3):287–305. [ Google Scholar ]
• Hammersley, Martyn. 2013. What is qualitative research? Bloomsbury Publishing.
• Hammersley M. What is ethnography? Can it survive should it? Ethnography and Education. 2018; 13 (1):1–17. [ Google Scholar ]
• Hammersley M, Atkinson P. Ethnography . Principles in practice . London: Tavistock Publications; 2007. [ Google Scholar ]
• Heidegger M. Sein und Zeit. Tübingen: Max Niemeyer Verlag; 2001. [ Google Scholar ]
• Heidegger, Martin. 1988. 1923. Ontologie. Hermeneutik der Faktizität, Gesamtausgabe II. Abteilung: Vorlesungen 1919-1944, Band 63, Frankfurt am Main: Vittorio Klostermann.
• Hempel CG. Philosophy of the natural sciences. Upper Saddle River: Prentice Hall; 1966. [ Google Scholar ]
• Hood JC. Teaching against the text. The case of qualitative methods. Teaching Sociology. 2006; 34 (3):207–223. [ Google Scholar ]
• James W. Pragmatism. New York: Meredian Books; 1907. [ Google Scholar ]
• Jovanović G. Toward a social history of qualitative research. History of the Human Sciences. 2011; 24 (2):1–27. [ Google Scholar ]
• Kalof L, Dan A, Dietz T. Essentials of social research. London: Open University Press; 2008. [ Google Scholar ]
• Katz J. Situational evidence: Strategies for causal reasoning from observational field notes. Sociological Methods & Research. 2015; 44 (1):108–144. [ Google Scholar ]
• King G, Keohane RO, Sidney S, Verba S. Scientific inference in qualitative research. Princeton: Princeton University Press; 1994. Designing social inquiry. [ Google Scholar ]
• Lamont M. Evaluating qualitative research: Some empirical findings and an agenda. In: Lamont M, White P, editors. Report from workshop on interdisciplinary standards for systematic qualitative research. Washington, DC: National Science Foundation; 2004. pp. 91–95. [ Google Scholar ]
• Lamont M, Swidler A. Methodological pluralism and the possibilities and limits of interviewing. Qualitative Sociology. 2014; 37 (2):153–171. [ Google Scholar ]
• Lazarsfeld P, Barton A. Some functions of qualitative analysis in social research. In: Kendall P, editor. The varied sociology of Paul Lazarsfeld. New York: Columbia University Press; 1982. pp. 239–285. [ Google Scholar ]
• Lichterman, Paul, and Isaac Reed I (2014), Theory and Contrastive Explanation in Ethnography. Sociological methods and research. Prepublished 27 October 2014; 10.1177/0049124114554458.
• Lofland J, Lofland L. Analyzing social settings. A guide to qualitative observation and analysis. 3. Belmont: Wadsworth; 1995. [ Google Scholar ]
• Lofland J, Snow DA, Anderson L, Lofland LH. Analyzing social settings. A guide to qualitative observation and analysis. 4. Belmont: Wadsworth/Thomson Learning; 2006. [ Google Scholar ]
• Long AF, Godfrey M. An evaluation tool to assess the quality of qualitative research studies. International Journal of Social Research Methodology. 2004; 7 (2):181–196. [ Google Scholar ]
• Lundberg G. Social research: A study in methods of gathering data. New York: Longmans, Green and Co.; 1951. [ Google Scholar ]
• Malinowski B. Argonauts of the Western Pacific: An account of native Enterprise and adventure in the archipelagoes of Melanesian New Guinea. London: Routledge; 1922. [ Google Scholar ]
• Manicas P. A realist philosophy of science: Explanation and understanding. Cambridge: Cambridge University Press; 2006. [ Google Scholar ]
• Marchel C, Owens S. Qualitative research in psychology. Could William James get a job? History of Psychology. 2007; 10 (4):301–324. [ PubMed ] [ Google Scholar ]
• McIntyre LJ. Need to know. Social science research methods. Boston: McGraw-Hill; 2005. [ Google Scholar ]
• Merton RK, Barber E. The travels and adventures of serendipity . A Study in Sociological Semantics and the Sociology of Science. Princeton: Princeton University Press; 2004. [ Google Scholar ]
• Mannay D, Morgan M. Doing ethnography or applying a qualitative technique? Reflections from the ‘waiting field‘ Qualitative Research. 2015; 15 (2):166–182. [ Google Scholar ]
• Neuman LW. Basics of social research. Qualitative and quantitative approaches. 2. Boston: Pearson Education; 2007. [ Google Scholar ]
• Ragin CC. Constructing social research. The unity and diversity of method. Thousand Oaks: Pine Forge Press; 1994. [ Google Scholar ]
• Ragin, Charles C. 2004. Introduction to session 1: Defining qualitative research. In Workshop on Scientific Foundations of Qualitative Research , 22, ed. Charles C. Ragin, Joane Nagel, Patricia White. http://www.nsf.gov/pubs/2004/nsf04219/nsf04219.pdf
• Rawls, Anne. 2018. The Wartime narrative in US sociology, 1940–7: Stigmatizing qualitative sociology in the name of ‘science,’ European Journal of Social Theory (Online first).
• Schütz A. Collected papers I: The problem of social reality. The Hague: Nijhoff; 1962. [ Google Scholar ]
• Seiffert H. Einführung in die Hermeneutik. Tübingen: Franke; 1992. [ Google Scholar ]
• Silverman D. Doing qualitative research. A practical handbook. 2. London: SAGE Publications; 2005. [ Google Scholar ]
• Silverman D. A very short, fairly interesting and reasonably cheap book about qualitative research. London: SAGE Publications; 2009. [ Google Scholar ]
• Silverman D. What counts as qualitative research? Some cautionary comments. Qualitative Sociology Review. 2013; 9 (2):48–55. [ Google Scholar ]
• Small ML. “How many cases do I need?” on science and the logic of case selection in field-based research. Ethnography. 2009; 10 (1):5–38. [ Google Scholar ]
• Small, Mario L 2008. Lost in translation: How not to make qualitative research more scientific. In Workshop on interdisciplinary standards for systematic qualitative research, ed in Michelle Lamont, and Patricia White, 165–171. Washington, DC: National Science Foundation.
• Snow DA, Anderson L. Down on their luck: A study of homeless street people. Berkeley: University of California Press; 1993. [ Google Scholar ]
• Snow DA, Morrill C. New ethnographies: Review symposium: A revolutionary handbook or a handbook for revolution? Journal of Contemporary Ethnography. 1995; 24 (3):341–349. [ Google Scholar ]
• Strauss AL. Qualitative analysis for social scientists. 14. Chicago: Cambridge University Press; 2003. [ Google Scholar ]
• Strauss AL, Corbin JM. Basics of qualitative research. Techniques and procedures for developing grounded theory. 2. Thousand Oaks: Sage Publications; 1998. [ Google Scholar ]
• Swedberg, Richard. 2017. Theorizing in sociological research: A new perspective, a new departure? Annual Review of Sociology 43: 189–206.
• Swedberg R. The new 'Battle of Methods'. Challenge January–February. 1990; 3 (1):33–38. [ Google Scholar ]
• Timmermans S, Tavory I. Theory construction in qualitative research: From grounded theory to abductive analysis. Sociological Theory. 2012; 30 (3):167–186. [ Google Scholar ]
• Trier-Bieniek A. Framing the telephone interview as a participant-centred tool for qualitative research. A methodological discussion. Qualitative Research. 2012; 12 (6):630–644. [ Google Scholar ]
• Valsiner J. Data as representations. Contextualizing qualitative and quantitative research strategies. Social Science Information. 2000; 39 (1):99–113. [ Google Scholar ]
• Weber, Max. 1904. 1949. Objectivity’ in social Science and social policy. Ed. Edward A. Shils and Henry A. Finch, 49–112. New York: The Free Press.

This paper is in the following e-collection/theme issue:

Published on 15.5.2024 in Vol 26 (2024)

Rapport Building in Written Crisis Services: Qualitative Content Analysis

Authors of this article:

Original Paper

• Laura Schwab-Reese 1 , BSc, MA, PhD   ; 
• Caitlyn Short 1 , BSc, MPH   ; 
• Larel Jacobs 2 , MEd, MSc   ; 
• Michelle Fingerman 2 , MSc  

1 Department of Public Health, Purdue University, West Lafayette, IN, United States

2 Childhelp, Scottsdale, AZ, United States

Corresponding Author:

Laura Schwab-Reese, BSc, MA, PhD

Department of Public Health

Purdue University

West Lafayette, IN, 47907

United States

Phone: 1 765 496 6723

Email: [email protected]

Background: Building therapeutic relationships and social presence are challenging in digital services and maybe even more difficult in written services. Despite these difficulties, in-person care may not be feasible or accessible in all situations.

Objective: This study aims to categorize crisis counselors’ efforts to build rapport in written conversations by using deidentified conversation transcripts from the text and chat arms of the National Child Abuse Hotline. Using these categories, we identify the common characteristics of successful conversations. We defined success as conversations where help-seekers reported the hotline was a good way to seek help and that they were a lot more hopeful, a lot more informed, a lot more prepared to address the situation, and experiencing less stress, as reported by help-seekers.

Methods: The sample consisted of transcripts from 314 purposely selected conversations from of the 1153 text and chat conversations during July 2020. Hotline users answered a preconversation survey (ie, demographics) and a postconversation survey (ie, their perceptions of the conversation). We used qualitative content analysis to process the conversations.

Results: Active listening skills, including asking questions, paraphrasing, reflecting feelings, and interpreting situations, were commonly used by counselors. Validation, unconditional positive regard, and evaluation-based language, such as praise and apologies, were also often used. Compared with less successful conversations, successful conversations tended to include fewer statements that attend to the emotional dynamics. There were qualitative differences in how the counselors applied these approaches. Generally, crisis counselors in positive conversations tended to be more specific and tailor their comments to the situation.

Conclusions: Building therapeutic relationships and social presence are essential to digital interventions involving mental health professionals. Prior research demonstrates that they can be challenging to develop in written conversations. Our work demonstrates characteristics associated with successful conversations that could be adopted in other written help-seeking interventions.

Introduction

Since the 1990s, mental health providers have explored how to support clients via internet-based communication [ 1 ]. Prior work suggests that young people may be particularly interested in these approaches, as digital communication feels more private and emotionally safe [ 2 ]. However, internet-based communication, particularly written communication, may have significant barriers for providers and clients, including the inability to express emotion and challenges in communicating clearly [ 2 ]. Currently, there is limited evidence on how to overcome these communication issues in counseling settings [ 3 ]. Understanding how to do so may help telehealth providers build stronger therapeutic relationships, thus improving the help-seeking process. Further, this understanding may help agencies improve services and training for providers.

Technology-Based Mental Health Interventions

Technology-based (ie, telehealth) mental health services may not be as effective as in-person services. One recent meta-analysis suggests that videoconferencing-based therapeutic relationships may be inferior to those developed during in-person therapy [ 4 ]. However, there may be times when in-person care is not accessible or feasible. Nearly half of people in the United States live in a mental health shortage area, and there are areas with less than 2 psychiatrists per 100,000 residents [ 5 , 6 ]. As a result, increasing access to mental health care may depend on telehealth approaches. Within telehealth studies, interventions retaining elements of human contact are more effective than entirely computer-based interventions [ 7 , 8 ].

Two critical aspects of the helping relationship, therapeutic relationship and social presence, may be challenged when engaging digitally. A therapeutic relationship based on mutual trust, respect, empathy, and positive regard is essential in counseling [ 9 ]. Hundreds of studies have confirmed the importance of this collaborative relationship [ 10 ]. For most help-seekers, confidence in the provider, including perceptions of empathy and expertise, is key to developing a strong relationship [ 11 - 13 ].

Social presence [ 14 ], the sense of connecting and being with another, is another element that may be compromised during digital communication. Social presence may also be defined as the degree to which the other person feels “real” [ 15 ]. Although it is a natural element of face-to-face counseling, telehealth providers may have to be intentional in building a social presence. When conversing with unknown entities through written technology, it is common to question whether the other person is a human or a bot [ 16 ], in part because people are not reliably able to differentiate between the two [ 17 ]. Some prior work suggests that social presence is an important aspect of digital helping relationships because it assists in building therapeutic partnerships, professional bonds, and open communication [ 18 , 19 ].

Much of the literature on telehealth counseling focuses on verbal communication via videoconferencing or phone [ 20 ]. Few studies examine written mental health counseling services, and there is reason to believe that spoken and written communication are substantially different. In a recent review of the digital therapeutic relationship, Bantjes and Slabbert [ 20 ] suggest practical strategies for establishing rapport in digital spaces, such as maintaining eye contact, having high-speed internet to avoid lags, and attending to lighting and microphone placement. These strategies improve the audio and visual cues, which are not applicable to written communication.

Written Interventions

The literature on written counseling is limited [ 21 , 22 ]. In the 1990s, a small group of mental health providers offered therapy via email [ 23 ]. This early work identified several possible strengths and limitations. It was helpful for clients to write about their feelings, and the anonymity of email allowed them to share more openly. This asynchronous approach also increased many individuals’ sense of control, as they could choose when and where to engage with the therapist. Conversely, building a relationship and understanding nuances could be difficult without the usual social cues [ 23 ]. Two more recent literature reviews support many of the impressions formed by the early adopters, although most of the studies had very small sample sizes [ 22 , 24 ].

Written interventions may be challenging for the provider and patient, and both experience similar challenges. One randomized controlled trial of a chat-based cognitive behavioral therapy demonstrated reduced depression symptoms after 10 sessions [ 25 ]. In a parallel qualitative study, participants reported mixed perceptions of the experience [ 26 ]. Some reported feeling more able to share openly and process because of the anonymous platform. Others felt it was challenging to develop a relationship and express complex feelings and thoughts via writing [ 26 ]. Another study assessed differences between email-based cognitive behavioral therapy and unguided treatment. The email and unguided programs had better outcomes than the wait-list control group for some, but not all, outcomes [ 27 , 28 ]. Other studies, with and without in-person or telephone comparison groups, showed similarly mixed results [ 29 , 30 ]. In one unpublished dissertation, counselors who provided email services reported feeling substantial anxiety due to uncertainty, limited sensory information, and concerns about misunderstanding clients’ intentions [ 31 ]. The lack of visual, verbal, and social cues was particularly challenging [ 31 ]. They often focused more on the tasks and transactional aspects of helping to manage these uncertain dynamics [ 31 ]. Many also talked about needing much more time than usual to build the therapeutic relationship, although it did eventually happen for most [ 31 ].

Beyond mental health counseling, some recent work has examined written communication for brief counseling and advocacy [ 3 , 32 , 33 ]. Overall, privacy, autonomy, control, anonymity, and accessibility are seen as benefits of written services [ 34 - 36 ]. Building social presence and connection is an important aspect of the experience [ 34 ]. Often this professional connection builds over time, but because the help-seeker and crisis counselor or advocate do not have an ongoing relationship, it may be particularly difficult to communicate adequately and build a relationship [ 2 , 30 , 37 , 38 ]. Correctly understanding sarcasm, humor, and other nuanced language is particularly challenging in these brief interventions [ 3 , 39 ]. Like mental health counseling, the impact of written crisis and advocacy services is unclear in the current literature and may depend on geographical location, counselor training, and the help-seekers’ situations [ 33 , 40 - 46 ].

Study Purpose

Overall, establishing a human connection based on a strong therapeutic relationship and social presence will likely result in more effective, acceptable interventions. Providing crisis services is complex, and the confines of written communication create additional challenges. Rapport-building is particularly difficult, and mistakes may cause the help-seeker to feel worse [ 47 ]. However, there are not yet best practices for building rapport in these conversations, as existing approaches to rapport-building often depend on verbal and nonverbal cues [ 48 ]. As part of a larger study focused on building best practices for written hotlines, we worked with a child maltreatment-focused text or chat hotline. This analysis aims to categorize crisis counselors’ efforts to build rapport and convey active listening in written conversations. Using these categories, we identified characteristics associated with successful conversations, as reported by help-seekers. This work provides an important foundation for how to build therapeutic relationships in written mental health and hotline services.

Data Source

The data for this study are from the PACTECH (Prevent Abuse of Children Text and Chat Hotline), the text and chat arm of the Childhelp National Child Abuse Hotline [ 49 ]. Since 1982, Childhelp has offered 24/7 phone-based hotline services focused on support and resources related to child maltreatment. In 2018, the hotline expanded to include text and chat capabilities. Crisis counselors are employees rather than volunteers. Most are master-level professionals with specialized training in hotline services and child maltreatment. After conducting a quantitative pilot evaluation for 2 years, hotline leadership partnered with the lead author to use qualitative and mixed method approaches to identify best practices for services. As part of the data sharing agreement, the lead author and her research team received access to deidentified transcripts and metadata from conversations that were purposefully selected to represent a wide range of experiences and perceived outcomes.

Ethical Considerations

The Purdue University Institutional Review Board approved the research protocol (IRB-2020-965). The service terms and conditions disclosed that data may be shared with researchers. As a secondary data analysis of deidentified data, additional consent from participants was not required by the Institutional Review Board. The contract teams from Purdue University and Childhelp negotiated the terms of the data sharing agreement, including data security and access. As a result of the data sharing agreement, the data may not be released publicly.

The sample consists of 314 purposely selected conversations out of the 1153 text and chat conversations during July 2020. In addition to maintaining the written transcript of the conversation for 60 days, Childhelp collects preconversation and postconversation surveys from the help-seekers. The preconversation surveys focus on help-seeker characteristics (ie, age, gender, state of residence, and referral source), while the postconversation survey focuses on their perceptions of the conversation (eg, do they feel more hopeful, less stressed, and more prepared). We used maximum variation sampling to capture diverse help-seekers and outcomes, although not necessarily in the proportions present in the overall data [ 50 ]. This approach is particularly useful when looking for diverse perspectives, as was the case for our study. We sampled based on the preconversation and postconversation surveys. In our sample, 297 (94.6%) help-seekers answered at least 1 presurvey question, and 263 (83.8%) answered at least 1 postconversation survey question. First, we selected conversations where help-seekers reported that they were satisfied, unsatisfied, or mixed. We also included some conversations without surveys to reduce survey response bias. Then, we reviewed the demographic characteristics of the selected conversations to ensure help-seekers of different ages, races or ethnicities, and genders were included in the sample. For example, most help-seekers are girls, so there were relatively few conversations with boys in our initial sample. We added additional conversations with boys to ensure the results were not only relevant to girls.

We analyzed and reported the findings from all 314 conversations. When reporting quotes, however, we were particularly interested in the 45 conversations where help-seekers reported in the postconversation survey that the hotline was a good way to seek help and that they were a lot more hopeful, a lot more informed, a lot more prepared to address the situation, and experiencing less stress. Except when specifically referencing less successful conversations, all example quotes come from these conversations, as they represent those most successful from the help-seekers’ perspectives. All quotes are reported verbatim from the conversations, including any errors.

We used qualitative content analysis to process the conversations. We used both inductive and deductive processes to develop the codes. The first draft of the coding frame was based on our work with child maltreatment–related conversations within the Crisis Text Line [ 51 - 53 ]. Then, we revised the framework based on the content of the conversations.

Our development process followed the adaptation of grounded theory described by Schreier [ 54 ]. The lead author and her graduate research assistant reviewed all the conversations. During a second review of the conversations, we took notes on commonalities within the conversations, emphasizing material not captured in the first draft of the codebook. As we refined the codebook, all team members met weekly to discuss emerging materials and define and develop codes. After completing the framework and definitions, we coded 30 conversations and met to compare the code applications. We discussed differences in coding and refined the framework with the entire team. Then, we coded 30 additional conversations and assessed the coder agreement. After the second round of pilot coding, we reached 95% agreement on the codes and moved to code the full data set. In sum, we had 127 codes in the codebook, which were applied 22,326 times. After coding all the conversations, we reviewed the materials within each code. This process followed the segmentation process described by Schreier [ 54 ], where coded materials are decontextualized and reviewed to identify commonalities and themes. Through this process, we also assessed whether we met saturation, which occurs when all categories have been identified in the data set. Schrier’s [ 54 ] definition of qualitative content analysis saturation is different from other forms of qualitative methods. In other forms of qualitative analysis, saturation refers to the point at which reviewing additional material does not provide new information. We informally assessed this type of saturation by examining whether all codes were used if we considered only half of the sample. We found that all codes were used when we reviewed 2 different randomly selected split samples, which suggests that few new insights would be gained if we added additional conversations to our sample. After conducting these checks, we categorized the conversations by the outcomes and focused on similarities and differences across the groups.

For this analysis, we focused on the codes related to rapport building and active listening conversations. There were two main types of approaches used by crisis counselors: (1) counseling approaches and (2) evaluation-based language. Active listening skills, otherwise known as attending skills, are how counselors build connections with clients, express empathy, and convey that they are listening [ 48 ]. These skills may be defined slightly differently; asking questions, paraphrasing, reflecting feelings, and interpreting or summarizing the situation are generally recognized skills. We added validation [ 55 ] and unconditional positive regard [ 56 ], which are also commonly incorporated into helping relationships. Evaluation-based language, such as praise and apologies, is commonly used by adults when talking with children [ 57 ]. These statements differ from other approaches because the counselor’s evaluation of the situation is included.

We also examined how these approaches differed between the help-seekers most satisfied with the conversation (ie, answered all after-conversation survey questions as “Yes”) and those who were least satisfied with the conversation. We intended to define the least satisfied as those who answered all the after-conversation survey questions as “No.” However, only 4 people fit that criterion, so we included all help-seekers who answered most of the questions negatively.

Research Team

The research team included the lead author, a graduate research assistant, and 2 collaborators at Childhelp. The lead author is a family violence prevention researcher with a PhD in public health and an MA in counseling. She has experience conducting qualitative analyses of written hotline transcripts. The graduate research assistant was a master of public health student and had worked on the lead author’s research team for 3 years. She had experience with qualitative child maltreatment research. The Childhelp collaborators have substantial experience in hotline counseling and leadership. One has an MS in counseling psychology. The second has an MS in family and human development and an MEd in guidance counseling.

Help-Seeker Characteristics

Overall, our sample of help-seekers was generally similar to Childhelp’s overall text and chat users ( Table 1 ) [ 58 , 59 ]. Help-seekers tended to be female, young, and seeking help for themselves. Overall, they were generally at least a little more hopeful, informed, and prepared to deal with the situation after the conversation ( Table 2 ).

a Includes children who were distressed but did not necessarily describe events consistent with maltreatment.

a Do you feel more positive or hopeful after this chat/text session?

b Did you get the information you needed from this chat or text session?

c Do you feel better prepared to deal with the situation after this chat or text session?

d Do you feel less stress after the chat or text session?

e Was using chat or text a good way for you to get help?

Active Listening Skills

Paraphrasing information and feelings.

When paraphrasing (387 times across 170 conversations), the crisis counselor repeated what was said by the help-seeker in a way that honed the focus of the conversation. Often, it included the most important words shared by the help-seeker, along with a shortened, clarified version of the essential information or feelings. For example, when seeking to understand the situation, a crisis counselor said, “It does not sound like she is able to listen to your needs and wants at this time.” At other times, the crisis counselor wanted to convey that they have been listening. Saying, “...you mentioned that they are screaming at him and from what you have said it sounds like they might be being really aggressive with him” demonstrated that they have been paying attention to the information shared.

Sometimes, the crisis counselors reflected the feelings shared by the help-seeker, saying things like, “That sounds like it can be frustrating from what you shared,” “it sounds very overwhelming and scary,” or “I can see how stressful this is.” In these situations, the crisis counselor was often distilling the feelings to support the help-seeker in identifying what is most bothering them about the situation or what feeling is driving their response to the situation. Once the help-seeker recognized the most troubling aspect of the situation, they were often more able to brainstorm ways to address it with the crisis counselor.

Interpretation

Interpreting the situation was also common (236 times across 125 conversations). Often, help-seekers were confused or had ambivalent thoughts about the situation. In these cases, they usually struggled to identify the next steps or reduce their emotional activation. By interpreting the situation, the crisis counselors offered a coherent overview of the situation and a different perspective. In most active listening skills, crisis counselors stayed quite close to the information provided by the help-seeker (eg, paraphrasing or reflecting what was said). When interpreting the situation, crisis counselors often included their perspectives on the situation with the intent of supporting the help-seeker to see themes or new ideas. For example, one help-seeker shared that their caregivers regularly say hurtful things about their gender identity and sexual orientation, scream and yell, and tell the help-seeker that they are a disappointment. In response, the crisis counselor said, “Sounds like it would be very hard to be happy living with people who treat you like that.” Although the help-seeker had not overtly shared about their unhappiness, this interpretation led to the help-seeker sharing about active suicidal ideation.

Open Questions

Open questions (208 across 124 conversations) served multiple purposes. At the beginning of the conversations, they invited the help-seeker to share about the experience. For example, “Could you tell me what’s going on?” or “What’s making you feel unsafe?” was used to begin the conversation in a nonthreatening way. As the conversation moved to explore the issues, open questions could elicit specific details (eg, “What’s happened since then?” and “What does that mean?”) or focus attention on feelings (eg, “How does it make you feel when your mom lashes out?” and “How are you feeling about all this happening?”).

Other Common Counseling Approaches

Validation was the most used approach to active listening (647 times across 226 conversations), and it took many forms depending on the situation. Throughout the conversations, it was used to affirm the help-seeker, their feelings, and their thoughts. For example, one counselor said, “It can be hard living in a house where you don’t feel supported and respected.” In this situation, the help-seeker had a difficult relationship with a father, who regularly called the help-seeker “overdramatic or a crybaby.” By validating the difficulty of feeling unsupported, the crisis counselor communicated that the help-seeker and their feelings were important.

In other instances, the crisis counselor validated the help-seeker’s perspectives about what was or was not appropriate behavior within families. In one instance, a help-seeker shared concerns about an older sibling’s treatment of an infant. The brother was rough with the infant and burned the infant with hot milk. In response, the crisis counselor said, “I can see why you would be concerned for the baby’s safety.” In doing so, the crisis counselor communicated that the help-seeker’s feelings were valid but without confirming that the infant was being maltreated. The crisis counselor had not seen evidence of the situation, so they could not accurately validate whether the infant was being maltreated. Simple phrases, such as “I hear you. This is difficult,” “That must be really hard for you,” and “It’s okay to feel stressed that is normal,” also validated the help-seeker and their perspectives.

Unconditional Positive Regard

Unconditional positive regard (102 times across 66 conversations) occurred when crisis counselors provided basic acceptance and support of the help-seeker, regardless of their behavior or things that have been done to them. Unconditional positive regard primarily focused on the abuse experience. It was common for counselors to say things like, “No one deserves to be abused” or “No one deserves to be treated like that.” These statements were often particularly well received by help-seekers, like this example:

You don’t deserve to be emotionally abused. It’s not o.k. [Counselor]

Thank you for saying that. You are the first person ive ever talked about this personally with. [Help-seeker]

Evaluation-Based Language

Evaluation-based language involved a judgment by the crisis counselor about whether an aspect of the help-seekers’ experiences was good (eg, behavior worthy of praise) or bad (eg, an apology for something that happened to the help-seeker). Evaluation and judgment are generally not a part of helping relationships [ 48 , 60 , 61 ] but are quite common when adults speak with children [ 57 , 62 ]. Although these approaches are not generally part of counseling relationships, there is nothing inherently wrong with using them intentionally.

Praise (268 times across 145 conversations) occurred when the crisis counselor conveyed that they approved of the help-seekers or their behavior. Sometimes, praise focused on the behaviors occurring during the conversations, like “Thank you for sharing with me” and “I’m glad you reached out today.” At other times, the praise centered on behaviors that they would do in the future, such as “Yes, I believe you’re doing the right thing by calling)” and “I think that will be a good move for you.”

Apologies (372 times across 213 conversations) tended to focus on the help-seeker’s situation or issues with the hotline. Apologies for the hotline were usually about a technical difficulty (eg, “sorry, our system is not working well”). Apologies about the help-seeker’s situation could be very broad, such as “I’m so sorry to hear about all of this” and “I’m so sorry that you’re having to go through this.” Apologies could also be specific to the situation, like “I am sorry to hear Mom yelled at you yesterday too.”

Differences Between Successful and Less Successful Conversations

There were some differences in active listening skills, other counseling skills, and evaluation-based language between successful and less successful conversations. Although the sample of successful and less successful conversations was too small for formal statistical analysis, some commonalities emerged. First, although conversations were approximately the same length, less successful conversations tended to have more statements that attended to rapport building. Second, there were also differences in how the counselors applied these approaches. Unlike the preceding sections, this section includes quotes from both successful and less successful conversations.

Overall, counselors in less successful conversations tended to be vague or to directly repeat what was said by the help-seeker. These differences were particularly apparent when counselors were paraphrasing, asking open questions, or apologizing. For example, paraphrasing in less successful conversations tended to be either very vague (eg, “It sounds like you are being hurt already”) or very specific (eg, “I am hearing you have some future plans to get a job and earn your own money...”). In the last example, the help-seeker used the same phrasing in their previous statement. Conversely, successful conversations tended to be specific without direct repetition (eg, “Sounds like they are something to help you cope”). Similarly, less successful conversations tended to include open questions that were either broad (eg, “What’s happening?”) or focused on clarifying how the crisis counselor could help (eg, “How are you hoping that I can help?”). Some successful conversations also included questions clarifying how the crisis counselor could help, but it was more common to ask more specific questions, like “What is it that you would like to vent about?” and “What are their thoughts on CPS involvement?” Finally, crisis counselors used generally vague apologies about the situation in less successful conversations. Saying things like “I am so sorry this happened to you” or “I’m sorry to hear that” was common. Although some successful conversations also included these types of apologies, it was more common to pair the apology with a specific reason, such as “I’m so sorry that you have been experiencing this for so long” or “I am sorry to hear Mama is sick.”

Principal Results

Overall, our study suggests that it is possible to build therapeutic relationships via a text and chat hotline with individuals seeking child maltreatment–related information and support. Approximately 15% (n=45) of our sample reported that the hotline was a good way to seek help and that they were a lot more hopeful, a lot more informed, a lot more prepared to address the situation, and experiencing less stress. However, our sample was intentionally selected to represent a wide range of help-seeker perceptions, so this does not indicate that 15% of the hotline’s help-seekers felt this way. Based on the 2022 Childhelp data report, about 85% of help-seekers reported getting the information they needed, 80% of help-seekers reported feeling more hopeful after the conversation, and 75% reported feeling better prepared to deal with the situation [ 59 ]. These percentages suggest that the hotline provides a well-received service.

Generally, counselors built rapport through active listening skills, other counseling techniques, and evaluation-based language (ie, apologies, praise). Through active listening skills and other counseling techniques, counselors often expressed that they were listening, wanted to understand the help-seekers, and cared for them. They expressed their approval or disapproval of the help-seekers and aspects of their experiences through evaluation-based language. Although there is nothing inherently wrong with using apologies and praise, they tend to be avoided in many therapeutic approaches. Praise may undermine intrinsic motivation (ie, internal drive) and reduce engagement in the process [ 63 - 65 ]. Further, these types of evaluation-based language are rooted in control, as they are given based on something that another individual (ie, the crisis counselor) deems desirable [ 63 ]. As a result, the help-seeker might seek praise by giving answers that they believe the crisis counselor wants to receive instead of accurate answers, which may reduce the benefit of the conversation. However, praise and compliments may be a quick way to build encouraging feelings [ 66 ]. As it is challenging to build relationships via writing, praise may be one way to build a relationship quickly. Additional research into the impact of evaluation-based language is necessary to understand its role in written crisis counseling.

There were some differences between successful and less successful conversations. Surprisingly, less successful conversations tended to include more attending language than successful conversations. However, there were differences in the ways that crisis counselors apply these techniques. Overall, the crisis counselors in successful conversations tended to be more specific and tailor their responses to the help-seekers. Possibly, counselors who gave tailored responses built rapport more quickly; thus, fewer attending statements were required. If this is the case, they could move to problem-solving more quickly, which may also contribute to help-seekers’ perceptions that they were more prepared to address the situation and were more informed. These tailored responses may increase help-seekers’ perceptions that the crisis counselor is invested in the conversation. Several help-seekers explicitly asked if they were speaking with a bot in this sample. Having tailored responses may increase crisis counselors’ social presence and reduce help-seekers’ concerns about whether they are “real.” As organizations consider using large language models and chatbots in these types of services, careful attention should be given to help-seekers’ perceptions about the service and its appropriateness for the audience. As the National Eating Disorders Association learned when its wellness chatbot began providing diet information, large language models trained on outside data may not be a good fit for conversations with help-seekers [ 67 ].

Limitations

Our work has several limitations, including some inherent to secondary data analysis. First, we could not speak with the help-seekers or the counselors about the conversations. Although we were able to identify similarities across well-received conversations, it is possible that other aspects of the conversations contributed to help-seekers’ perceptions. Second, we do not know how these conversations shaped long-term outcomes. Moreover, it is difficult to follow up with help-seekers, as evidenced by the 6% response rate to a 2-week follow-up survey conducted by the National Domestic Violence Hotline [ 68 ]. Further, many of the help-seekers in this sample indicated that it is unsafe to speak aloud about their experiences, so qualitative data collection with this sample would likely have an even lower response rate. It would be more feasible to speak with counselors about their experiences, but their perspectives may be disconnected from those of the help-seekers. Despite this limitation, we incorporated the help-seekers’ perspectives through the postconversation survey, which is more than is usually possible in secondary data analysis.

Our work may not generalize to conversations unrelated to child maltreatment. As a child maltreatment–specific hotline, all conversations included elements of child maltreatment. Conversations about other topics may require other approaches. However, our results are consistent with prior work on building rapport in other forms of counseling [ 48 , 55 , 56 ], so it is reasonable to expect these findings would translate to written conversations about other topics.

Comparison With Prior Work

To the best of our knowledge, there is no other work examining specific ways to build a therapeutic relationship in written mental health counseling or crisis counseling. However, the ways that crisis counselors attended to the dynamics of the conversations were generally like those found in in-person counseling [ 48 , 55 , 56 ].

Telehealth approaches to counseling may be particularly important for young people experiencing maltreatment. Other formal resources, such as law enforcement, schools, and child protection systems, often fail to respond adequately [ 53 , 69 , 70 ]. Further, internet-based approaches, particularly written approaches, are highly acceptable to young people experiencing maltreatment [ 69 ]. In our sample and past research, children shared that they could not call resources because an audible conversation would cause parents to know they were seeking help. In work conducted with Crisis Text Line, it was common for young people sharing child maltreatment to report that the abuse escalated when parents discovered their attempts to seek help. Written, anonymous communication that is available 24/7 may be a safer way for these young people to seek help. Thus, written communication may be particularly important for children in unsafe homes.

There is also limited evidence on how to respond when young people share maltreatment experiences. Regardless of the ability to impact or end the maltreatment, individuals who receive a child maltreatment disclosure need to receive an appropriate, supportive response [ 71 - 74 ]. Supportive responses encourage the young person experiencing maltreatment to reframe their experience, which substantially reduces the likelihood of poor outcomes otherwise associated with maltreatment [ 75 ]. Conversely, unsupportive experiences often have long-lasting consequences [ 74 , 76 ]. Receiving a hurtful or unsupportive response increases the likelihood that the young person will experience more significant physical and mental health issues [ 74 , 76 , 77 ]. Unfortunately, many young people receive unsupportive responses to their disclosures [ 53 , 70 ]. Often, they report that others, particularly adults, do not believe them and are unwilling to help [ 70 , 78 ]. These experiences reduce their willingness to seek help or share their experiences in the future [ 70 ]. Our work suggests that responding to these disclosures adequately in written conversations is possible.

Our work also contributes to a small body of literature on using text and chat hotlines to provide services to people experiencing violence more generally. Michigan State University added chat services to its existing sexual assault support and advocacy hotline. Their evaluation was consistent with many of the benefits and limitations of other forms of written counseling, including challenges with nuance, misunderstanding written language, and communicating empathy [ 3 ]. However, the format also gave help-seekers a greater sense of control [ 3 ]. Another study focused on agencies providing digital violence-related support and advocacy services [ 32 ]. This work also emphasized the importance of clear communication and building rapport, although help-seeker perceptions of these factors were not assessed [ 32 ].

Conclusions

Building therapeutic relationships and social presence are important components of digital interventions involving mental health professionals. Prior research suggests that they can be challenging to develop in written conversations. Our work demonstrates characteristics of conversations associated with greater satisfaction among help-seekers. These findings may be adopted by other organizations building mental health or support interventions that include written communication. However, additional research is needed to identify how to train providers to adopt these strategies while also tailoring their approach to the help-seeker. Further, our findings may inform future work with large language models, including how large language models could contribute to these interventions. However, future research is needed to understand how help-seekers would interface with these methods and to ensure that the models consistently convey appropriate, supportive information.

Acknowledgments

This project was supported by the Children’s Bureau (CB) and Administration for Children and Families (ACF) of the US Department of Health and Human Services (HHS) as part of a financial assistance award in the amount of US $6,000,000 that was 100% funded by the CB and ACF of the HHS. The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement by, the CB and ACF of the HHS or the US government. For more information, please visit administrative and national policy requirements.

Conflicts of Interest

None declared.

• Murphy LJ, Mitchell DL. When writing helps to heal: e-mail as therapy. Br J Guid Counc. 1998;26(1):21-32. [ CrossRef ]
• King R, Bambling M, Lloyd C, Gomurra R, Smith S, Reid W, et al. Online counselling: the motives and experiences of young people who choose the internet instead of face to face or telephone counselling. Couns Psychother Res. 2006;6(3):169-174. [ CrossRef ]
• Moylan CA, Carlson ML, Campbell R, Fedewa T. "It's hard to show empathy in a text": developing a web-based sexual assault hotline in a college setting. J Interpers Violence. 2022;37(17-18):NP16037-NP16059. [ CrossRef ] [ Medline ]
• Norwood C, Moghaddam NG, Malins S, Sabin-Farrell R. Working alliance and outcome effectiveness in videoconferencing psychotherapy: a systematic review and noninferiority meta-analysis. Clin Psychol Psychother. 2018;25(6):797-808. [ CrossRef ] [ Medline ]
• Cheng N, Mohiuddin S. Addressing the nationwide shortage of child and adolescent psychiatrists: determining factors that influence the decision for psychiatry residents to pursue child and adolescent psychiatry training. Acad Psychiatry. 2022;46(1):18-24. [ CrossRef ] [ Medline ]
• Morales DA, Barksdale CL, Beckel-Mitchener AC. A call to action to address rural mental health disparities. J Clin Transl Sci. 2020;4(5):463-467. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Richards D, Richardson T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev. 2012;32(4):329-342. [ CrossRef ] [ Medline ]
• Spek V, Cuijpers P, Nyklícek I, Riper H, Keyzer J, Pop V. Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychol Med. 2007;37(3):319-328. [ CrossRef ] [ Medline ]
• Torous J, Hsin H. Empowering the digital therapeutic relationship: virtual clinics for digital health interventions. NPJ Digit Med. 2018;1:16. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Karver MS, Handelsman JB, Fields S, Bickman L. Meta-analysis of therapeutic relationship variables in youth and family therapy: the evidence for different relationship variables in the child and adolescent treatment outcome literature. Clin Psychol Rev. 2006;26(1):50-65. [ CrossRef ] [ Medline ]
• Finsrud I, Nissen-Lie HA, Vrabel K, Høstmælingen A, Wampold BE, Ulvenes PG. It's the therapist and the treatment: the structure of common therapeutic relationship factors. Psychother Res. 2022;32(2):139-150. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Elliott R, Bohart AC, Watson JC, Murphy D. Therapist empathy and client outcome: an updated meta-analysis. Psychotherapy (Chic). 2018;55(4):399-410. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nienhuis JB, Owen J, Valentine JC, Black SW, Halford TC, Parazak SE, et al. Therapeutic alliance, empathy, and genuineness in individual adult psychotherapy: a meta-analytic review. Psychother Res. 2018;28(4):593-605. [ CrossRef ] [ Medline ]
• Short J, Williams E, Christie B. The Social Psychology of Telecommunications. Hoboken, NJ. Jon Wiley & Sons; 1976.
• Gunawardena CN. Social presence theory and implications for interaction and collaborative learning in computer conferences. Int J Educ Telecommun. 1995;1(2):147-166. [ FREE Full text ]
• Batish R. Voicebot and Chatbot Design: Flexible Conversational Interfaces with Amazon Alexa, Google Home, and Facebook Messenger. Birmingham, UK. Packt Publishing Ltd; 2018.
• Warwick K, Shah H. The importance of a human viewpoint on computer natural language capabilities: a turing test perspective. AI Soc. 2016;31(2):207-221. [ CrossRef ]
• Lopez A. An investigation of the use of internet based resources in support of the therapeutic alliance. Clin Soc Work J. 2014;43(2):189-200. [ CrossRef ]
• Holmes C, Foster V. A preliminary comparison study of online and face-to-face counseling: client perceptions of three factors. J Technol Hum Serv. 2012;30(1):14-31. [ CrossRef ]
• Bantjes J, Slabbert P. The digital therapeutic relationship: retaining humanity in the digital age. In: Stein DJ, Fineberg NA, Chamberlain SR, editors. Mental Health in a Digital World. Amsterdam. Elsevier; 2022;223-237.
• Berger T. The therapeutic alliance in internet interventions: a narrative review and suggestions for future research. Psychother Res. 2017;27(5):511-524. [ CrossRef ] [ Medline ]
• Richards D, Viganó N. Online counseling: a narrative and critical review of the literature. J Clin Psychol. 2013;69(9):994-1011. [ CrossRef ] [ Medline ]
• Chechele PJ, Stofle G. Individual therapy online via email and internet relay chat. In: Anthony K, editor. Technology in Counselling and Psychotherapy: A Practitioner's Guide. London. Palgrave Macmillan; 2003;39-58.
• Stoll J, Müller JA, Trachsel M. Ethical issues in online psychotherapy: a narrative review. Front Psychiatry. 2019;10:993. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kessler D, Lewis G, Kaur S, Wiles N, King M, Weich S, et al. Therapist-delivered internet psychotherapy for depression in primary care: a randomised controlled trial. Lancet. 2009;374(9690):628-634. [ CrossRef ] [ Medline ]
• Beattie A, Shaw A, Kaur S, Kessler D. Primary-care patients' expectations and experiences of online cognitive behavioural therapy for depression: a qualitative study. Health Expect. 2009;12(1):45-59. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vernmark K, Lenndin J, Bjärehed J, Carlsson M, Karlsson J, Oberg J, et al. Internet administered guided self-help versus individualized e-mail therapy: a randomized trial of two versions of CBT for major depression. Behav Res Ther. 2010;48(5):368-376. [ CrossRef ] [ Medline ]
• Andersson G, Paxling B, Roch-Norlund P, Östman G, Norgren A, Almlöv J, et al. Internet-based psychodynamic versus cognitive behavioral guided self-help for generalized anxiety disorder: a randomized controlled trial. Psychother Psychosom. 2012;81(6):344-355. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dowling M, Rickwood D. Investigating individual online synchronous chat counselling processes and treatment outcomes for young people. Adv Ment Health. 2015;12(3):216-224. [ CrossRef ]
• King R, Bambling M, Reid W, Thomas I. Telephone and online counselling for young people: a naturalistic comparison of session outcome, session impact and therapeutic alliance. Couns Psychother Res. 2006;6(3):175-181. [ CrossRef ]
• Francis-Smith C. Email counselling and the therapeutic relationship: a grounded theory analysis of therapists' experiences [dissertation]. University of the West of England. 2014. URL: https:/​/uwe-repository.​worktribe.com/​index.php/​preview/​806312/​Thesis%20amended%20for%20repository.​pdf [accessed 2024-04-17]
• Wood L, Hairston D, Schrag RV, Clark E, Parra-Cardona R, Temple JR. Creating a digital trauma informed space: chat and text advocacy for survivors of violence. J Interpers Violence. 2022;37(19-20):NP18960-NP18987. [ CrossRef ] [ Medline ]
• Gould MS, Chowdhury S, Lake AM, Galfalvy H, Kleinman M, Kuchuk M, et al. National suicide prevention lifeline crisis chat interventions: evaluation of chatters' perceptions of effectiveness. Suicide Life Threat Behav. 2021;51(6):1126-1137. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gibson K, Cartwright C. Young people's experiences of mobile phone text counselling: balancing connection and control. Child Youth Serv Rev. 2014;43:96-104. [ CrossRef ]
• Evans WP, Davidson L, Sicafuse L. Someone to listen: increasing youth help-seeking behavior through a text-based crisis line for youth. J Community Psychol. 2013;41(4):471-487. [ CrossRef ]
• Predmore Z, Ramchand R, Ayer L, Kotzias V, Engel C, Ebener P, et al. Expanding suicide crisis services to text and chat. Crisis. 2017;38(4):255-260. [ CrossRef ] [ Medline ]
• Chardon L, Bagraith KS, King RJ. Counseling activity in single-session online counseling with adolescents: an adherence study. Psychother Res. 2011;21(5):583-592. [ CrossRef ] [ Medline ]
• Bambling M, King R, Reid W, Wegner K. Online counselling: the experience of counsellors providing synchronous single-session counselling to young people. Couns Psychother Res. 2008;8(2):110-116. [ CrossRef ]
• Rodda SN, Lubman DI, Cheetham A, Dowling NA, Jackson AC. Single session web-based counselling: a thematic analysis of content from the perspective of the client. Br J Guid Counc. 2015;43(1):117-130. [ CrossRef ]
• Fukkink RG, Hermanns JMA. Children's experiences with chat support and telephone support. J Child Psychol Psychiatry. 2009;50(6):759-766. [ CrossRef ] [ Medline ]
• Fukkink R, Hermanns J. Counseling children at a helpline: chatting or calling? Am J Community Psychol. 2009;37(8):939-948. [ CrossRef ]
• Sindahl TN, van Dolen W. Texting at a child helpline: how text volume, session length and duration, response latency, and waiting time are associated with counseling impact. Cyberpsychol Behav Soc Netw. 2020;23(4):210-217. [ FREE Full text ] [ CrossRef ] [ Medline ]
• van Dolen W, Weinberg CB. Child helplines: how social support and controllability influence service quality and well-being. J Serv Mark. 2017;31(4/5):385-396. [ CrossRef ]
• van Dolen W, Weinberg CB. An empirical investigation of factors affecting perceived quality and well-being of children using an online child helpline. Int J Environ Res Public Health. 2019;16(12):2193. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Navarro P, Bambling M, Sheffield J, Edirippulige S. Exploring young people's perceptions of the effectiveness of text-based online counseling: mixed methods pilot study. JMIR Ment Health. 2019;6(7):e13152. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Thompson LK, Sugg MM, Runkle JR. Adolescents in crisis: a geographic exploration of help-seeking behavior using data from crisis text line. Soc Sci Med. 2018;215:69-79. [ CrossRef ] [ Medline ]
• Fildes D, Williams K, Bradford S, Grootemaat P, Kobel C, Gordon R. Implementation of a pilot SMS-based crisis support service in Australia. Crisis. 2022;43(1):46-52. [ CrossRef ] [ Medline ]
• Ivey AE, Packard NG, Ivey MB. Basic Attending Skills. San Diego, CA. Cognella; 2018.
• The Childhelp National Child Abuse Hotline. Childhelp. 2020. URL: https://www.childhelp.org/hotline/ [accessed 2024-04-17]
• Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm Policy Ment Health. 2015;42(5):533-544. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cash SJ, Murfree L, Schwab-Reese L. "I'm here to listen and want you to know I am a mandated reporter": understanding how text message-based crisis counselors facilitate child maltreatment disclosures. Child Abuse Negl. 2020;102:104414. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schwab-Reese L, Kanuri N, Cash S. Child maltreatment disclosure to a text messaging-based crisis service: content analysis. JMIR Mhealth Uhealth. 2019;7(3):e11306. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schwab-Reese LM, Cash SJ, Lambert NJ, Lansford JE. "They aren't going to do jack shit": text-based crisis service users' perceptions of seeking child maltreatment-related support from formal systems. J Interpers Violence. 2022;37(19-20):NP19066-NP19083. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schreier M. Qualitative Content Analysis in Practice. Thousand Oaks, CA. Sage; 2012.
• Linehan MM. Validation and psychotherapy. In: Bohart AC, Greenberg LS, editors. Empathy Reconsidered: New Directions in Psychotherapy. Washington, DC. American Psychological Association; 1997;353-392.
• Wilkins P. Unconditional positive regard reconsidered. Br J Guid Counc. 2010;28(1):23-36. [ CrossRef ]
• Brummelman E, Crocker J, Bushman BJ. The praise paradox: when and why praise backfires in children with low self-esteem. Child Dev Perspect. 2016;10(2):111-115. [ CrossRef ]
• Wolfersteig W, Moreland D, Diaz M, Gotlieb E. Prevent Abuse of Children Text and Chat Hotline (PACTECH) project: semi-annual data report. Childhelp. Scottsdale, Arizona.; 2022. URL: https://www.childhelphotline.org/wp-content/uploads/2022/05/PACTECH-Data-Report-April-2022.pdf [accessed 2024-04-17]
• Hotline impact report. Childhelp. 2022. URL: https://www.childhelphotline.org/wp-content/uploads/2022/10/Hotline-Impact-Report-FY22.pdf [accessed 2024-04-17]
• Nicholas A, Pirkis J, Reavley N. What responses do people at risk of suicide find most helpful and unhelpful from professionals and non-professionals? J Ment Health. 2022;31(4):496-505. [ CrossRef ] [ Medline ]
• Rogers CR. A Way of Being. Boston, MA. Houghton Mifflin Harcourt; 1980.
• Brummelman E, Nelemans SA, Thomaes S, de Castro BO. When parents' praise inflates, children's self-esteem deflates. Child Dev. 2017;88(6):1799-1809. [ CrossRef ] [ Medline ]
• Kelsey J. The negative impact of rewards and ineffective praise on student motivation. ESSAI. 2011;8(1):24. [ FREE Full text ]
• Kakinuma K, Nishiguti F, Sonoda K, Tajiri H, Tanaka A. The negative effect of ability-focused praise on the "praiser's" intrinsic motivation: face-to-face interaction. Front Psychol. 2020;11:562081. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pellecchia M, Nuske HJ, Straiton D, Hassrick ME, Gulsrud A, Iadarola S, et al. Strategies to engage underrepresented parents in child intervention services: a review of effectiveness and co-occurring use. J Child Fam Stud. 2018;27(10):3141-3154. [ CrossRef ]
• Landrum RE, Gurung RA, Nolan SA, McCarthy MA, Dunn DS. Everyday Applications of Psychological Science: Hacks to Happiness and Health. Milton Park, UK. Routledge; 2022.
• McCarthy L. A wellness chatbot is offline after its 'harmful' focus on weight loss. The New York Times. 2023. URL: https://www.nytimes.com/2023/06/08/us/ai-chatbot-tessa-eating-disorders-association.html [accessed 2024-04-17]
• McDonnell K, Nagaraj N, Fuerst M. Short-term outcomes following contact with the national domestic violence hotline and loveisrespect. U.S. Department of Health & Human Services. 2020. URL: https:/​/www.​acf.hhs.gov/​opre/​report/​short-term-outcomes-following-contact-national-domestic-violence-hotline-and [accessed 2024-04-17]
• Al-Eissa MA. Utilization of child helpline (CHL) among adolescents in Saudi Arabia: results from a national survey. Child Fam Soc Work. 2019;24(1):84-89. [ CrossRef ]
• Tucker S. Listening and believing: an examination of young people's perceptions of why they are not believed by professionals when they report abuse and neglect. Child Soc. 2011;25(6):458-469. [ CrossRef ]
• Collin-Vézina D, De La Sablonnière-Griffin M, Palmer AM, Milne L. A preliminary mapping of individual, relational, and social factors that impede disclosure of childhood sexual abuse. Child Abuse Negl. 2015;43:123-134. [ CrossRef ] [ Medline ]
• Goodman-Brown TB, Edelstein RS, Goodman GS, Jones DPH, Gordon DS. Why children tell: a model of children's disclosure of sexual abuse. Child Abuse Negl. 2003;27(5):525-540. [ CrossRef ] [ Medline ]
• Jensen TK, Gulbrandsen W, Mossige S, Reichelt S, Tjersland OA. Reporting possible sexual abuse: a qualitative study on children's perspectives and the context for disclosure. Child Abuse Negl. 2005;29(12):1395-1413. [ CrossRef ] [ Medline ]
• Palmer SE, Brown RA, Rae-Grant NI, Loughlin MJ. Responding to children's disclosure of familial abuse: what survivors tell us. Child Welfare. 1999;78(2):259-282. [ Medline ]
• Briere J, Jordan CE. Violence against women: outcome complexity and implications for assessment and treatment. J Interpers Violence. 2004;19(11):1252-1276. [ CrossRef ] [ Medline ]
• Arata CM. To tell or not to tell: current functioning of child sexual abuse survivors who disclosed their victimization. Child Maltreatment. 1998;3(1):63-71. [ CrossRef ]
• Palo AD, Gilbert BO. The relationship between perceptions of response to disclosure of childhood sexual abuse and later outcomes. J Child Sex Abus. 2015;24(5):445-463. [ CrossRef ] [ Medline ]
• Cossar J, Belderson P, Brandon M. Recognition, telling and getting help with abuse and neglect: young people's perspectives. Child Youth Serv Rev. 2019;106:104469. [ CrossRef ]

Edited by T de Azevedo Cardoso; submitted 19.08.22; peer-reviewed by K Zhang, V Franzoni, B Li, Z Aghaei; comments to author 28.03.23; revised version received 21.08.23; accepted 26.03.24; published 15.05.24.

©Laura Schwab-Reese, Caitlyn Short, Larel Jacobs, Michelle Fingerman. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Product development: Leveraging qualitative research to meet customer expectations

Explore four stages in product development where implementing qualitative research can provide deep insights, guiding companies to better meet consumer needs and stay ahead of the competition.

Tips for collecting customer feedback  

Editor’s note: Kimberly Pate is managing director, corporate research, at Arlington, Va.-based market research firm Hanover Research. This is an edited version of a post that originally appeared here . 

Customer expectations for their products are higher than ever, and companies are struggling to keep up.

A recent survey found that one in three consumers is struggling to find products that meet their needs. Companies that do not gather customer insights and incorporate them into product development will lose customers to their competitors. In fact, that same survey showed that 72% of customers are willing to try new products to find one that meets their needs.

Companies have several options for collecting customer feedback on products, the most common of which are customer surveys. However, there are a few stages of the product development process where companies can benefit from going beyond a survey by gathering qualitative data. This data allows companies to discover rich and valuable insights that can direct areas for future research or development.

What is qualitative product feedback?

Qualitative product feedback involves asking customers questions directly. The two most common formats are customer interviews and focus groups. This approach allows companies to gather in-depth feedback and ask follow-up questions to delve deeper into the reasoning behind responses. The open-ended questions center on a product’s value, features and product experience allowing companies to build products and features customers want.

Though asking interview questions may seem straightforward and relatively easy, it is critical to enlist an experienced facilitator. A skilled moderator ensures that questions are asked in a way that gets the desired insights without influencing customers’ responses. They also need to correctly code and accurately interpret the responses without bias or assumptions.

By gathering and incorporating qualitative product feedback companies can:

• Understand customer perceptions and preferences on various aspects of the company and its products.
• Gather unanticipated feedback that would not be collected from a formatted survey.
• Clarify customers’ opinions by asking for follow-up questions that provide more context.

Understanding when to collect product feedback

There are four key stages of product development where qualitative feedback is critical: initial product development, product refinement, pre-launch and post-launch. 

1. Initial product development.  Qualitative research can help narrow and refine potential product concepts during the initial idea-generation phase of product development. Its approaches can produce a detailed picture of customers’ experiences with a product and reveal unmet needs and untapped opportunities.

For example, a company looking to update a product can interview current customers about the existing product and gather opinions on potential features. Gathering direct feedback at this stage allows companies to identify the products and features customers are most interested in and why.

2. Product refinement.   Customer feedback is also critical in the product refinement stage. Even if you confirmed a product is in demand in earlier development stages, your produced product may not align with what customers are willing to buy.

Gathering feedback on proposed elements (features, pricing, etc.,) and allowing customers to beta test and evaluate products helps companies determine if the proposed product is viable and identify what additional refinement is necessary.

In this phase, engaging an audience that spans different age groups, ethnicities, income levels and genders is critical to take cultural differences and considerations into account that may not be top of mind for design or marketing teams.

3. Pre-launch.   Companies need to fine-tune how they will market the new product before releasing it. Qualitative feedback at this stage can help companies identify what themes and elements of the product resonate most with customers.

A common example is focus groups that are shown new messaging or ads before launch. This allows researchers to collect a group’s reaction to new concepts and spark conversation on what resonates and what’s falling off the mark. Not only does collecting direct customer feedback help companies optimize their messaging, but it can also produce customer quotes that can showcase the product’s value, enhancing marketing and sales strategies.

4. Post-launch.   Most rigorous post-launch reviews include some type of customer experience survey to evaluate the product’s performance. By conducting follow-up qualitative research, companies can collect additional data and context to clarify the results of the post-launch surveys.

For example, if a typically loyal and engaged customer segment expresses dissatisfaction with functionality or a lack of desire to buy a new product, qualitative research can uncover the reasons why and allow a company to direct future product improvements.

Qualitative feedback can uncover potential product pitfalls

While it is hard to foresee every potential product pitfall, a well-designed research plan that includes qualitative feedback can identify potential concerns with product features at different stages in product development and bring to light new perceptions and interpretations that companies can leverage to further enhance their products.

The challenges and benefits of AI-powered customer service Related Categories: Consumers, Consumer Research, CX/UX-Customer/User Experience Consumers, Consumer Research, CX/UX-Customer/User Experience, Artificial Intelligence / AI, Research Industry, Customer Satisfaction Studies, Retailing

How hype analysis lets companies find value in customer excitement Related Categories: Consumers, Consumer Research, Qualitative Research Consumers, Consumer Research, Qualitative Research, Research Industry, Artificial Intelligence / AI, Marketing Research-General, Psychological/Emotion Research, Quantitative Research, Social Media Research

How to maximize the benefits and avoid the pitfalls of corporate advocacy Related Categories: Consumers, Consumer Research, CX/UX-Customer/User Experience Consumers, Consumer Research, CX/UX-Customer/User Experience, Research Industry, Brand Identity, LGBTQIA+, Strategy Research

Leveraging AI to unlock qualitative research at scale Related Categories: Consumers, Consumer Research, Qualitative Research Consumers, Consumer Research, Qualitative Research, Artificial Intelligence / AI, Hybrid Research (Qual/Quant), Quantitative Research, Research Industry, Attitude/Usage Studies, Data Visualization/Infographics, Sampling

• Open access
• Published: 09 May 2024

Getting an outsider’s perspective - sick-listed workers’ experiences with early follow-up sessions in the return to work process: a qualitative interview study

• Martin Inge Standal 1 , 2 ,
• Vegard Stolsmo Foldal 1 ,
• Lene Aasdahl 1 , 3 ,
• Egil A. Fors 1 &
• Marit Solbjør 1  

BMC Health Services Research volume  24 , Article number:  609 ( 2024 ) Cite this article

117 Accesses

Metrics details

The aim of this study was to explore how early follow-up sessions (after 14 and 16 weeks of sick leave) with social insurance caseworkers was experienced by sick-listed workers, and how these sessions influenced their return-to-work process.

A qualitative interview study with sick-listed workers who completed two early follow-up sessions with caseworkers from the Norwegian Labor and Welfare Administration (NAV). Twenty-six individuals aged 30 to 60 years with a sick leave status of 50–100% participated in semi-structured interviews. The data was analyzed with thematic analysis.

Participants’ experiences of the early follow-up sessions could be categorized into three themes: (1) Getting an outsider’s perspective, (2) enhanced understanding of the framework for long term sick-leave, and (3) the empathic and personal face of the social insurance system. Meeting a caseworker enabled an outsider perspective that promoted critical reflection and calibration of their thoughts. This was experienced as a useful addition to the support many received from their informal network, such as friends, family, and co-workers. The meetings also enabled a greater understanding of their rights and duties, possibilities, and limitations regarding welfare benefits, while also displaying an unexpected empathic and understanding perspective from those working in the social insurance system.

For sick-listed individuals, receiving an early follow-up session from social insurance caseworkers was a positive experience that enhanced their understanding of their situation, and promoted reflection towards RTW. Thus, from the perspective of the sick-listed workers, early sessions with social insurance caseworkers could be a useful addition to the overall sickness absence follow-up.

Peer Review reports

Introduction

Returning to work (RTW) from long-term sick leave is a complex and multifaceted process [ 1 ]. Prolonged sick leave has been linked to poorer health [ 2 ] and is thought to increase the psychosocial obstacles for RTW [ 3 ]. Therefore, early RTW interventions have been suggested to be central to the RTW-process [ 3 ]. Long-term sickness absence is often understood as sick-leave beyond 4–8 weeks of work absence. Most workers return to work on their own within the first few months of absence [ 4 ] and interventions in the following weeks, can improve the likelihood of RTW for those remaining [ 5 , 6 , 7 , 8 ]. Furthermore, in the context of long-term sick leave, interventions contributing to earlier RTW can be highly cost-effective [ 9 , 10 ].

In Norway, the responsibility of early sick-leave follow-up is shared between the general practitioner (GP), who certify sick leave and assess remaining work capabilities, and the employer who should make accommodations at the workplace to facilitate RTW [ 11 ]. The employer has the main responsibility to assist their employees back to work but many employers lack the resources to properly facilitate RTW [ 12 ], and GPs may not see RTW as one of their primary focuses [ 13 ]. Thus, the existing system for early RTW follow-up in Norway, which largely rely on the cooperation between employer and employee, may not be sufficient to promote RTW [ 14 ]. This means that more effort to promote RTW might be needed. For instance, in other legislative systems RTW coordinators that assist other stakeholders and facilitate the RTW process are frequently used [ 15 , 16 ]. In Norway, there are no formal RTW coordinator roles, and the task of facilitating cooperation between stakeholders, such as the employer, healthcare services and the sick-listed, fall on social insurance caseworkers working in the Norwegian Labour and Welfare Administration (NAV). They have a counseling role in sickness absence follow-up by providing support for the employer and sick-listed worker, but they also act as a controller of eligibility for sickness benefits [ 17 ]. Ordinarily, there are few meeting points between the sick listed worker and their NAV caseworker, and most sick listed workers have their first meeting with NAV when they have been sick-listed for six months.

The impact of RTW coordinators is contested. A broad systematic review determined that RTW coordinators had little effect on RTW [ 18 ]. However, face-to-face meetings with RTW coordinators have also been shown to increase RTW rates [ 19 ]. Evidence from Norway suggest that meetings between NAV caseworkers, sick-listed individuals and other stakeholders at 26 weeks could be cost-beneficial for RTW [ 20 ]. Caseworkers reviewing possibilities and barriers to RTW has also been found to improve the caseworkers’ knowledge of the sick-listed’s situation and consequently improved RTW rates in the following months [ 21 ]. Social insurance caseworkers could thus be in a position to provide additional case-management and support in the earlier stages of sick leave. Researchers have also suggested that NAV should play a more active part in the earlier phases of long-term sick leave [ 22 ]. Similarly, caseworkers have also called for being involved earlier in the RTW process [ 23 ]. In their experience, the longer workers are on sick leave, the harder it is to facilitate RTW [ 14 ]. Moreover, sick-listed individuals in Norway also expect some form of NAV involvement in the early stage of long-term sick-leave [ 24 ].

In a recent study, sick-listed workers experienced that early follow-up sessions where NAV caseworkers used motivational interviewing helped normalize their situation and improved their beliefs in their RTW plan [ 25 ]. Given the extensive resources required to implement and adopt motivational interviewing in a social insurance setting [ 23 ], it is also useful to know how early additional follow-up sessions without a guided focus is experienced, and how they could fit within the standard follow-up for workers on long term sick-leave.

Thus, the aim of this study was to investigate how sick-listed workers experienced early additional follow-up sessions with NAV and how they experienced the influence of the sessions on their RTW process.

Materials and methods

The present study was based on 26 semi-structured individual interviews with sick-listed workers participating in a randomized controlled trial (RCT). The aim of the RCT was to evaluate the effect of motivational interviewing as an instrument for caseworkers at NAV in facilitating RTW for sick-listed workers [ 26 ]. The early follow-up sessions, which this paper focuses on served as an active control group.

The Norwegian welfare system and sickness absence follow-up

In Norway, employees are entitled to full wage benefits in the case of sickness absence, from the first day of absence to a maximum period of 52 weeks. Sick leave is in most cases certified by the individual’s general practitioner. During the first 16 days, the employer is responsible for the payment, while the rest is paid for by the National Insurance Scheme through NAV [ 27 ]. The employer must initiate a follow-up plan in cooperation with the employee before the end of the fourth week of sick leave and is responsible for arranging a meeting with the sick-listed worker within the seventh week of absence, including other stakeholders if relevant. If the employer facilitates work-related activities, the sick-listed worker is required to participate. NAV is responsible for arranging a meeting including the employer and the sick-listed worker at 26 weeks of sick leave. The attendance of the sick-listed worker’s GP is optional. However, the GP is obliged to attend if NAV deems it necessary for the coordination of the RTW process. This is the only obligatory meeting point between a sick listed worker and NAV. Additional meetings can also be held if one or more of the stakeholders find it necessary. Thus, the sick-listed worker may also ask for a meeting with NAV to coordinate a plan for RTW outside this schedule [ 27 ]. After 12 months of sick leave, it is possible to apply for the more long-term benefits, work assessment allowance and permanent disability pension.

The early follow-up sessions

The early follow-up sessions for this study were in addition to ordinary follow-up and consisted of two counseling sessions held at 14 and 16 weeks of sick leave. The sessions, offered by a NAV caseworker, lasted a maximum of 60 min and were in addition to standard NAV follow-up. During the first session, the caseworker opted to map out the sick-listed worker’s work situation, their relationship to their employer, their RTW plan, treatment plans and work ability, in addition to informing the sick-listed worker about their rights and duties as sick-listed. The caseworkers also informed about possible RTW measures through NAV. The second session focused on following up on the topics discussed in the first session, as well as focusing on any changes in the sick-listed workers’ situation that might have occurred between the first and second session.

These sessions functioned as an active control group in the RCT and were designed to be similar to the motivational interviewing sessions provided in terms of dose and timing. Caseworkers providing the active control sessions were separate from those providing the motivational interviewing sessions and they received no formal motivational interviewing training. They were, however, recruited voluntarily to the study from the same NAV-office as those performing the motivational interviewing sessions. Caseworkers were not randomized to group in the RCT and thus joined knowing that they would provide early follow-up using their usual methods.

Study population and recruitment

The study population consisted of sick-listed workers who were enrolled in the RCT. Eligible participants were sick listed workers aged 18–60 years old, living in central Norway, with any diagnoses. Their sick-leave status at the time of inclusion in the RCT were 50–100% for at least 8 weeks. Exclusion criteria were pregnancy-related sick-leave, unemployment, and being self-employed. To be eligible to participate in this interview study the sick-listed worker had to have been randomized to the active control group in the RCT and completed the early follow-up sessions. Eligible participants were identified by NAV and contact info was forwarded to the researchers. A member of the project group invited the participants to take part in the research interview by phone. A total of 40 individuals were invited to participate in the interview study, of which 14 did not answer, declined the invitation, or did not show up at the interview. Twenty-six individuals participated in the interviews, including 19 women and 7 men aged 31–61. Participants showed diversity in their self-reported reasons for being sick listed, with 11 having mental health disorders, 8 having musculoskeletal disorders, and 7 individuals reported other disorders.

Data collection

We conducted semi-structured individual interviews which allowed the participants to provide in-depth descriptions of their experiences. Interviews were based on an interview guide with five main questions concerning their experiences during sick leave, the RTW process, experiences of the two follow-up sessions, and whether these sessions led to any changes during their RTW process. The interviews were conducted between November 2018 and September 2019 and were audio recorded and transcribed verbatim. The duration of the interviews ranged from 35 min to 65 min.

Data analysis

For our data analysis, we used reflexive thematic analysis which is a method for identifying, analyzing, and reporting patterns within qualitative data [ 28 ]. Thematic analysis is a flexible approach which allows researchers to interpret the data through a six phased recursive process, moving back and forth between phases to build themes from codes. The first step of the analysis involved becoming familiar with the data [ 28 ] where transcripts of all interviews were read and re-read by authors VSF, MIS and MS to get an overall impression of the contents. Preliminary codes and patterns were identified, as a start of the coding process. The second step of the analysis was the coding process, where items of interest related to the aim were coded by author VSF. These codes were then used to create core categories for further development of initial themes [ 28 ]. The third step was combining the codes into initial themes, which is a data reducing process which allows interpretation from the researchers [ 28 ]. Initial themes were discussed among all authors. The fourth step was reviewing the generated themes and checking them against the coded data, in order to further expand or revise the developed themes [ 28 ]. When reviewing the generated themes against the coded data, the preliminary analysis indicated a tendency where participants who received good support and follow-up by their employer considered the early follow-up sessions by NAV as less useful than the participants who lacked support and follow-up by their employer. However, a coding of the interviews focusing on this aspect showed no clear tendency of favoring early follow-up sessions based on high or low employer support. Thus, the initial themes were further developed into the three main themes which will be presented below. All authors had several meetings to discuss, define and refine the final themes in order to tell a coherent and compelling story about the data [ 28 ].

All participants received written and oral information about the study and gave their written consent before the interview started. Participants were informed that participation was voluntary and that they could withdraw from the study at any time, if the data had not been anonymized and integrated in the analysis.

The study was approved by the Regional Committee for Medical and Health Research Ethics in Southeast Norway (No: 2016/2300).

Regarding receiving the two sessions, the participants had overall positive experiences with the content and timing of the first session. The second session, however, was frequently experienced as an unnecessary repetition of the first as much of the content was already covered. In the following we present our results of participants’ experience of the early follow-up sessions as three themes: (1) Getting an outsider’s perspective, (2) enhanced understanding of the framework for long term sick-leave, and (3) the empathic and personal faces of the social insurance system.

Getting an outsider’s perspective

Participants describe the meetings with a NAV caseworker as a positive experience that also challenged their current view of their situation and their RTW process. Meeting a NAV caseworker was experienced as an arena where they received guidance from an individual who examined their situation through an outsider’s perspective. NAV caseworkers provided support and encouragement, but also asked critical questions regarding their situation and their plans for RTW.

“… we talked primarily about my situation, and I felt like I was allowed to talk to someone unbiased, without you know, being limited in the conversation. And I felt like I could talk about those things important to me. […] it turned out to be a good dialogue where she pulled me further, and made me think about a couple of things” - Interview 3 .

The outside perspective was described as useful due to the participants’ context prior to the meeting, which was their everyday lives with friends, colleagues, family, GPs, and employers. This informal network was described as significant supporters during the sick leave and served an important role as confidants to whom the sick-listed worker could talk about their difficult or confusing situation. The formal support from the employer varied, where some experienced several supportive phone calls and meetings with the employer during their sick leave, while others had only had a single formal meeting. Having support from the employer was experienced as crucial for a good RTW process, and absence of support and a distant relationship to the employer led to a difficult RTW process with negative emotions and reduced belief in their RTW capabilities. Participants also experience that being able to talk freely with the employer could be difficult, and that they would be held accountable if confiding about difficulties in RTW. Thus, in contrast to the largely supportive informal network, and the restrained environment surrounding employer-support, meeting the NAV caseworkers provided a useful outside perspective. When describing the early sessions compared to their overall sick leave follow-up, participants described meeting NAV as a calibration of their thoughts and providing a new perspective compared to their other RTW supporters.

Enhanced understanding of the framework for long term sick leave

An important element of the first meeting was receiving information about rights, obligations as sick-listed, and the frame for future economic benefits. Receiving information about potential future loss of income and the possibility of having disability benefits was novel and useful for the participants. For some, this information led to new reflections on how being long-term sick-listed would have financial consequences, thereby providing another push for returning to work. For one participant, information about possible future loss of income provoked a feeling of panic and challenged her sense of identity.

“I remember that when he started talking about work assessment allowance, I panicked a bit. Because I couldn’t identify with that category. But at the same time, I thought, okay, it’s good information to have you know.” - Interview 2 .

Furthermore, the participants were happy with agenda of the first meeting where the NAV caseworkers focused on short-term, as well as long-term plans for RTW and gave personal feedback about participants’ RTW plan. Included in the short- and long-term focus was receiving information from NAV about available RTW measures and interventions. Whether the sick-listed workers were planning on a fast or slow paced RTW plan, they experienced that receiving support on their plans and ideas strengthened their beliefs in managing RTW. NAV caseworkers also presented different strategies relating to possible accommodations at work, such as adjusting workload, work tasks and working time. Information such as the possibility of adjusting their time spent at work and their sick-leave status enabled the sick-listed workers to reorient their perception towards returning to work.

“… in a way I hadn’t thought so carefully about when it’s smart to return and in what percentage. Because when I got that deal with the GP where I was still 100% sick-listed but could regulate it myself within 20% it was the first step to beginning to test myself.” - Interview 10 .

Participants received individually tailored information regarding the possibility of flexibility in the time spent at work and the amount of work they produced (i.e., sick leave percentage does not reflect hours spent at work, only the amount of work one does). This was highlighted as new and important information that was experienced as a contribution towards RTW.

The empathetic and personal face of the social insurance system

All study participants had taken part in two sessions with a caseworker from NAV. Prior to these sessions, NAV had been perceived as difficult to get in touch with and some feared that cooperation with NAV would be either difficult or absent. However, when meeting the NAV-caseworker, their fears were diminished and to their surprise, they were met by supportive, accommodating, and friendly caseworkers.

“NAV got a face; a personal face and NAV was no longer the huge colossus. The anonymous colossus that no one understands that just spews rules you have to relate to, which can be very … I can react with fear, I get afraid. “Am I doing this right?” you know. Am I following all these rules that I do not understand? What happened when NAV suddenly became a person was that they were on my side. They helped me, and it was possible to talk to NAV. A nice person helped me instead of rules that try to hinder me that I have to follow.” – Interview 19 .

The early follow-up sessions were experienced as more relevant when comparing them with other follow-up with their employer or later meetings with other caseworkers from NAV.

“I wished that the other later conversations and meetings [with NAV] was comprised of the same understanding and competence that this counselor had. So that is what I’m sitting here thinking, that this was a star example of how one should be met, you know.” – Interview 5 .

The positive experiences of the early follow-up session were due to the understanding atmosphere that was created by the caseworkers, who was perceived as genuinely interested in their situation, cooperative and jointly reflecting about their RTW plan. Caseworkers asked questions about aspects of the participants’ lives that could be related to their situation as a sick-listed worker, and they appeared attentive when listening. This led to the experience of being met as a whole person and contributed to the early follow-up sessions being experienced as an arena where they felt acknowledged and cared for.

“So, I came to NAV in high spirits and was well received and excellently informed and had a great conversation, really. Felt like I was to a psychologist, but that may be what I needed, and a neutral third-party that I feel listens to me. […] that is good medicine I think - that someone listens to what I say.” – Interview 6 .

Although some of the topics were considered quite personal, the sick-listed workers mostly experienced a respectful and reassuring dialogue with the caseworker. This personal and accommodating approach was overall positive for the participants, where the caseworkers matched their personality and behavior quite well. For several participants, the early follow-up sessions were considered almost therapeutic:

“You know, I experienced [the sessions] very positively. I met a counselor that displayed a lot of understanding and for me it was almost therapeutic to talk to her. I sat there and though wow, either something has happened to NAV or this person is hand-picked for me.” – Interview 5 .

On the other hand, talking about health-related topics such as psychological well-being while being sick-listed could be emotionally straining. Some considered this therapeutic approach to a session as out of place. When these participants experienced questions from the caseworker as too personal, they saw their caseworker as intrusive and prying into personal issues. Such situations emphasized caseworkers’ position as representative for the social insurance system with its function for control and surveillance.

The results from this study showed that the participants experienced early follow-up sessions by social insurance caseworkers as positive. They described the value of receiving an outside view of their situation and practical information about being on sick leave, while at the same time being met with a supportive and respectful demeanor. These aspects were described as promoting reflection on their situation and their thoughts on RTW. The second session was, however, frequently experienced as superfluous and a repetition of the first session. This can also be seen in the results, where participants to a large degree describe the benefits of simply meeting an understanding NAV caseworker who provide practical information and helps them reflect on their situation, which could be achieved through a single session.

The sick-listed workers who experienced good supportive contact in the current study considered this to be instrumental for their RTW process. Comparatively, some sick-listed workers experienced an absence of support and a distant relationship to their employer. Supportive contact with the employer and workplace has been found to be critical in preventing work disability [ 29 , 30 ] and important for facilitating RTW for sick-listed workers [ 31 ]. The negative impact of lack of workplace support on RTW has also been demonstrated previously [ 29 , 30 , 32 , 33 ]. In the present study, participants to a large degree experienced support from their surrounding network. However, the type of support received has been suggested to play a role, where validation and empathy-based support may promote coping behaviors that are beneficial for RTW, while solicitousness could be detrimental through encouraging illness behavior [ 34 ]. Thus, an outside view of the situation at an early stage of sick leave may be sensible. The present study show that regardless of the support from other stakeholders, getting a second opinion was an exceedingly positive experience which provided an avenue for reflection upon their current situation and their plans going forward. Openness in the dialogue with caseworkers has also been identified as relevant to experience a fair and acceptable sick leave process [ 35 ], and RTW-coordinators arguably are in a position to provide an unbiased perspective on RTW plans, independent of the other stakeholders [ 36 ].

One of the benefits experienced in the present study was a greater understanding of the framework of sick leave. Social insurance literacy relates to the sick-listed individual’s understanding of the social insurance system, how to act on the information obtained, and why decisions surrounding their situation are being made [ 36 , 37 ]. As individuals rarely have thorough knowledge of the social insurance system prior to sick-listing, social insurance literacy is also concerned with how well the system enables them to understand the process [ 38 ]. Previous research has suggested that enhancing the workers’ understanding of the system could improve their feelings of legitimacy and fairness in the process [ 35 ], and the present study provides some insight into how RTW coordinators could be experienced as helpful in this regard. Participants also described the clear agenda, in which the RTW plan was discussed, as useful. Examining barriers and facilitators for RTW and creating and re-examining the RTW plan is considered crucial to facilitate the RTW process [ 36 ]. The RTW-coordinator has also previously been suggested to have an important role in ensuring joint understanding and communication surrounding expectations and the context of long-term sick leave [ 39 ]. Thus, findings suggest that providing information on the system while inviting the sick-listed workers to reflect on their situation was experienced positively and possibly increased their social insurance literacy. However, the results in this study could also partly be explained by the context. It is possible that by voluntarily enrolling caseworkers and sick-listed workers in a research trial, a more individualized atmosphere was created in contrast to a more standardized RTW-follow-up scheme.

Nonetheless, experiences of the participants in the present study were largely positive and participants experienced being met with respect and understanding. Müssener and colleagues [ 40 ] also concluded in their study that how sick-listed individuals are treated affects their self-confidence and their perception of their ability to RTW. They suggest that the structural prerequisites for the RTW professional, such as having a gatekeeper role compared to a supportive role, seems to impact their treatment of sick-listed people [ 40 ]. The potential of the RTW coordinator to establish a good and trustful relationship with emphasis on the sick-listed workers’ motivation and resources in the RTW process has also been found to be important for RTW [ 41 , 42 , 43 ]. The conflicting roles of social insurance officers, being both facilitators and authority of benefits could potentially hinder the development of this relationship [ 41 ]. As identified by Karlsson [ 36 ], interactions between social insurance caseworkers and clients were perceived as either supportive or mistrustful. In the present study, the results suggest that the NAV-caseworkers may have had a stronger focus on the facilitator role, rather than the role of being gatekeepers of benefits.

In a recent study we found that sick-listed workers’ experienced early follow-up sessions with NAV as a positive experience and that it increased their RTW self-efficacy, when the caseworkers used motivational interviewing [ 25 ]. In the current study, the sick-listed workers met with NAV caseworkers who were not using motivational interviewing but rather using their ordinary approach when assisting sick-listed individuals. However, the experiences of the participants were strikingly similar in these two studies. The caseworker and sick-listed worker engaged in cooperatively reflections about when and how to RTW, which the sick-listed workers experienced to be valuable support and feedback for their RTW process. There may be some parallels to research on clinical psychotherapy, where studies have shown that the method of therapy may not be as important as the characteristics of the therapist [ 44 , 45 ]. For instance, having interpersonal skills that enable a therapeutic alliance in which one can effectively promote a course of action and create belief in change is considered vital [ 46 ]. Thus, being met by an emphatic and understanding caseworker may be beneficial, regardless of approach to the sessions. The present study supports the notion that having an early face-to-face meeting with a NAV caseworker can be a positive experience in the RTW-process for long-term sick-listed workers.

Whether positive experiences with the social insurance system translates into RTW-rates is still debatable. On the one hand, a recent systematic review on RTW coordinators’ impact on RTW found that work absence duration and intervention costs were reduced when sick-listed workers had face-to-face contact with a RTW coordinator [ 19 ]. On the other hand, previous research has discussed the lock-in effect of programs through the social insurance service, which may lead to longer periods on sick leave [ 47 ]. Similarly, regular contact with the social insurance office has been shown to have a negative effect on RTW-rates, which may indicate the risk of developing a ‘social insurance career’ [ 48 ]. In a previous study we found that sick-listed individuals also experienced that caseworkers frequently recommended a slower RTW pace than what was originally planned [ 25 ]. Furthermore, even though the experiences of early contact with NAV-caseworkers in the present study was positive, no impact on RTW outcomes could be identified in the trial results [ 49 ].

Strengths and limitation

A strength of the current study was the use of semi-structured interviews. This allowed the participants to elaborate and describe their experience of the early follow-up sessions in relation to their RTW process. In order to explore and uncover different experiences and nuances of the early follow-up sessions, a broad exploratory approach was used with a heterogenous sample. All analytical steps and preliminary findings were discussed with members of the research group to strengthen the interpretations, and final results were validated by all authors. The study also has some limitations. First, caseworkers performing the sessions voluntarily submitted to take part in the RCT and to undertake the follow-up sessions. They received no motivational interviewing training but were recruited from the same offices that those in the motivational interviewing group. This means there could be selection where caseworkers who were more interested in early follow-up were more likely to take part. Furthermore, there could be a spillover effect in the office, where caseworkers receiving motivational interviewing training pass on their knowledge to others in the office. We do however believe the impact of the spillover effect was small as recruitment was from one of the largest NAV-offices in Norway, and our previous study show that extensive training in motivational interviewing was required to achieve beginning proficiency [ 23 ].

Some participants in the study may have failed to recall information and details from the early follow-up sessions, since the interviews were conducted several months (ranging from 1 to 6 months) after the intervention. Although none of the participants expressed any difficulties in the interviews, there is a risk that the sick-listed workers held back information if they feared there would be consequences for their benefits. The current study recruited participants from a RCT with a response rate of approximately 15%. From this sample, the current nested study had a response rate of 65%. This indicates a selection bias, where participants agreeing to participate have different characteristics than those declining. Such bias might reduce variety in the experiences of the early follow-up sessions.

Sick-listed workers considered additional early sessions with social insurance caseworkers as a positive addition to ordinary RTW follow-up. Having these early face-to-face meeting with respectful and accommodating caseworkers that also asked critical questions about participants’ situation, provided sick-listed workers with an outside perspective that enabled them to reflect on their situation. This was experienced as a useful addition to their friends, family and colleagues who were largely supportive. Furthermore, the sessions provided the sick-listed workers with an arena for receiving practical information on the framework of sick-leave follow-up, such as rights, obligations, and possibilities in strategies for RTW. This enabled them to adjust their plan towards RTW. Finally, having individual face-to-face sessions also changed participants’ perceptions of NAV from a anonymous entity to emphatic and understanding individuals, who seemed genuinely interested in assisting them back to work. Thus, from the perspective of the sick-listed individuals, early additional follow-up sessions were experienced as exceedingly positive and would be welcomed in addition to standard follow-up.

Data availability

To protect the anonymity of the participants, the datasets generated and analyzed during the current study are not publicly available. Redacted versions are available from the corresponding author upon reasonable request.

Abbreviations

General practitioner

Norwegian Labor and Welfare Administration

• Return to work

Randomized controlled trial

Andersen MF, Nielsen KM, Brinkmann S. Meta-synthesis of qualitative research on return to work among employees with common mental disorders. Scand J Work Environ Health. 2012;38(2):93–104.

Article   PubMed   Google Scholar  

Waddell G, Burton AK. Is work good for your health and wellbeing? London, UK: The Stationery Office; 2006.

Google Scholar  

Aylward SM. Overcoming barriers to recovery and return to work: towards behavioral and cultural change. In: Schultz I, Gatchel R, editors. Handbook of return to work. Boston, MA: Springer; 2016. https://doi.org/10.1007/978-1-4899-7627-7_7 .

Chapter   Google Scholar  

McLeod CB, Reiff E, Maas E, Bültmann U. Identifying return-to-work trajectories using sequence analysis in a cohort of workers with work-related musculoskeletal disorders. Scand J Work Env Hea. 2018;44(2):147–55. https://doi.org/10.5271/sjweh.3701 .

Article   Google Scholar  

Bültmann U, Sherson D, Olsen J, Hansen CL, Lund T, Kilsgaard J. Coordinated and tailored work rehabilitation: a randomized controlled trial with economic evaluation undertaken with workers on sick leave due to musculoskeletal disorders. J Occup Rehabil. 2009;19(1):81–93.

Palmer KT, Harris EC, Linaker C, Barker M, Lawrence W, Cooper C, Coggon D. Effectiveness of community-and workplace-based interventions to manage musculoskeletal-related sickness absence and job loss: a systematic review. Rheumatology. 2012;51(2):230–42. https://doi.org/10.1093/rheumatology/ker086 .

Roelen CA, Norder G, Koopmans PC, Van Rhenen W, Van Der Klink JJ, Bültmann U. Employees sick-listed with mental disorders: who returns to work and when? J Occup Rehabil. 2012;22(3):409–17. https://doi.org/10.1007/s10926-012-9363-3 .

Article   CAS   PubMed   Google Scholar  

Steenstra IA, Anema JR, Van Tulder MW, Bongers PM, De Vet HC, Van Mechelen W. Economic evaluation of a multi-stage return to work program for workers on sick-leave due to low back pain. J Occup Rehabil. 2006;16(4):557–78. https://doi.org/10.1007/s10926-006-9053-0 .

Dagenais S, Caro J, Haldeman S. A systematic review of low back pain cost of illness studies in the United States and internationally. Spine J. 2008;8(1):8–20. https://doi.org/10.1016/j.spinee.2007.10.005 .

van Duijn M, Eijkemans MJ, Koes BW, Koopmanschap MA, Burton KA, Burdorf A. The effects of timing on the cost-effectiveness of interventions for workers on sick leave due to low back pain. Occup Environ Med. 2010;67(11):744–50. https://doi.org/10.1136/oem.2009.049874 .

Norwegian Directorate of Health. Sykmelderveileder. Nasjonal veileder. [Guidance for sickness certification. National guideline]. https://www.helsedirektoratet.no/veiledere/sykmelderveileder . Accessed 19.03.2024.

Holmgren K, Ivanoff SD. Supervisors’ views on employer responsibility in the return to work process. A focus group study. J Occup Rehabil. 2007;17(1):93–106.

Mazza D, Brijnath B, Singh N, Kosny A, Ruseckaite R, Collie A. General practitioners and sickness certification for injury in Australia. BMC Fam Pract. 2015;16:100. https://doi.org/10.1186/s12875-015-0307-9 .

Article   PubMed Central   PubMed   Google Scholar  

Ose SO, Dyrstad K, Brattlid I, Slettebak R, Jensberg H, Mandal R, Lippestad J, Pettersen I. Oppfølging av sykmeldte–fungerer dagens regime? [Follow-up of sick-listed – does today’s regime work?]. Trondheim, NO: SINTEF; 2013.

Shaw W, Hong QN, Pransky G, Loisel P. A literature review describing the role of return-to-work coordinators in trial programs and interventions designed to prevent workplace disability. J Occup Rehabil. 2008;18(1):2–15.

MacEachen E, McDonald E, Neiterman E, et al. Return to work for Mental Ill-Health: a scoping review exploring the impact and role of return-to-work coordinators. J Occup Rehabil. 2020;30:455–65. https://doi.org/10.1007/s10926-020-09873-3 .

Article   CAS   PubMed Central   PubMed   Google Scholar  

Norwegian Ministry of Labour and Social Affairs. (2016). NAV i en ny tid – for arbeid og aktivitet. Meld. St. 33 (2015–2016). [NAV in a new age – for work and activity] Retrieved from https://www.regjeringen.no Accessed 19.03.2024.

Vogel N, Schandelmaier S, Zumbrunn T, Ebrahim S, de Boer WE, Busse JW, Kunz R. Return-to‐work coordination programmes for improving return to work in workers on sick leave. Cochrane Database Syst Rev. 2017(3). https://doi.org/10.1002/14651858.CD011618.pub2 .

Dol M, Varatharajan S, Neiterman E, McKnight E, Crouch M, McDonald E, Malachowski C, Dali N, Giau E, MacEachen E. Systematic review of the impact on return to work of return-to-work coordinators. J Occup Rehabil. 2021;31(4):675–98.

Markussen S, Røed K, Schreiner RC. Can compulsory dialogues nudge sick-listed workers back to work? Econ J. 2018;128(610):1276–303. https://doi.org/10.1111/ecoj.12468 .

Nossen JP, Brage S. Aktivitetskrav Og midlertidig stans av sykepenger - hvordan påvirkes sykefraværet? [Activity demands and temporary stop in paid sick leave – how is sickness absence affected?]. Arbeid Og Velferd. 2015;3.

Mandal R, Jakobsen Ofte H, Jensen C, Ose SO. Hvordan fungerer arbeidsavklaringspenger (AAP) som ytelse og ordning? [How does work assessment allowance work as a benefit and arrangement?]. Trondheim, Norway: SINTEF; 2015.

Foldal VS, Solbjør M, Standal MI, Fors EA, Hagen R, Bagøien G. Mfl. Barriers and facilitators for implementing motivational interviewing as a return to work intervention in a Norwegian Social Insurance setting: a mixed methods process evaluation. J Occup Rehabil. 2021;31(4):785–95.

Standal MI, Foldal VS, Hagen R, Aasdahl L, Johnsen R, Fors EA. Mfl. Health, Work, and Family strain–psychosocial experiences at the early stages of long-term sickness absence. Front Psychol. 2021;12:596073.

Foldal VS, Standal MI, Aasdahl L, Hagen R, Bagøien G, Fors EA. Mfl. Sick-listed workers’ experiences with motivational interviewing in the return to work process: a qualitative interview study. BMC Public Health. 2020;20(1):1–10.

Aasdahl L, Foldal VS, Standal MI, Hagen R, Johnsen R, Solbjør M. Mfl. Motivational interviewing in long-term sickness absence: study protocol of a randomized controlled trial followed by qualitative and economic studies. BMC Public Health. 2018;18(1):1–8.

Norwegian Labour and Welfare Administration. Sickness benefits for employees. 2023. Retrieved from https://www.nav.no/en/home/benefits-and-services/Sickness-benefit-for-employees . Accessed 19.03.2024.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Shaw WS, Robertson MM, Pransky G, McLellan RK. Employee perspectives on the role of supervisors to prevent workplace disability after injuries. J Occup Rehabil. 2003;13(3):129–42.

Jansen J, van Ooijen R, Koning PWC, et al. The role of the employer in supporting work participation of workers with disabilities: a systematic literature review using an Interdisciplinary Approach. J Occup Rehabil. 2021;31:916–49. https://doi.org/10.1007/s10926-021-09978-3 .

Buys NJ, Selander J, Sun J. Employee experience of workplace supervisor contact and support during long-term sickness absence. Disabil Rehabil. 2019;41(7):808–14.

Buys N, Wagner S, Randall C, Harder H, Geisen T, Yu I, Hassler B, Howe C, Fraess-Phillips A. Disability management and organizational culture in Australia and Canada. Work. 2017;57(3):409–19.

Kristman VL, Shaw WS, Boot CR, Delclos GL, Sullivan MJ, Ehrhart MG. Researching complex and multi-level workplace factors affecting disability and prolonged sickness absence. J Occup Rehabil. 2016;26(4):399–416.

Reme SE. Common Mental disorders and work: barriers and opportunities. Handbook of disability, work and health. 2020:467 – 81. In: Bültmann U, Siegrist J, editors. Handbook of disability, work and health. Handbook Series in Occupational Health Sciences. Volume 1. Cham, CH: Springer; 2020.

Karlsson E, Legitimacy. and comprehensibility of work-related assessments and official decisions within the sickness insurance system [Internet] [PhD dissertation]. [Linköping]: Linköping University Electronic Press; 2022. (Linköping University Medical Dissertations). https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-183867 .

Kristman VL, Boot CR, Sanderson K, Sinden KE, Williams-Whitt K. Implementing best practice models of return to work. Handbook of disability, work and health. 2020:1–25. In: Bültmann U., Siegrist J, editors Handbook of Disability, Work and Health. Handbook Series in Occupational Health Sciences, vol 1. Cham, CH: Springer; 2020.

Christian Ståhl EA, Karlsson J, Sandqvist G, Hensing S, Brouwer EF, Ellen MacEachen. Social insurance literacy: a scoping review on how to define and measure it. Disabil Rehabil. 2021;43(12):1776–85. https://doi.org/10.1080/09638288.2019.1672111 .

Karlsson EA, Hellgren M, Sandqvist JL, et al. Social Insurance Literacy among the sick-listed—A study of clients’ comprehension and self-rated system comprehensibility of the Sickness Insurance System. J Occup Rehabil. 2024. https://doi.org/10.1007/s10926-023-10166-8 .

Corbière M, Mazaniello-Chézol M, Bastien MF, et al. Stakeholders’ role and actions in the return-to-work process of workers on sick-leave due to Common Mental disorders: a scoping review. J Occup Rehabil. 2020;30:381–419. https://doi.org/10.1007/s10926-019-09861-2 .

Müssener U, Ståhl C, Söderberg E. Does the quality of encounters affect return to work? Lay people describe their experiences of meeting various professionals during their rehabilitation process. Work. 2015;52(2):447–55.

Andersen MF, Nielsen K, Brinkmann S. How do workers with common mental disorders experience a multidisciplinary return-to-work intervention? A qualitative study. J Occup Rehabil. 2014;24(4):709–24.

Haugli L, Maeland S, Magnussen LH. What facilitates return to work? Patients experiences 3 years after occupational rehabilitation. J Occup Rehabil. 2011;21(4):573–81.

Scharf J, Angerer P, Müting G, Loerbroks A. Return to work after common mental disorders: a qualitative study exploring the expectations of the involved stakeholders. Int J Environ Res Public Health. 2020;17(18):6635.

Saxon D, Firth N, Barkham M. The relationship between therapist effects and therapy delivery factors: therapy modality, dosage, and non-completion. Adm Policy Ment Health Ment Health Serv Res. 2017;44(5):705–15.

Wampold BE, Bolt DM. Therapist effects: Clever ways to make them (and everything else) disappear. Psychother Res. 2006;16(02):184–7.

Anderson T, McClintock AS, Himawan L, Song X, Patterson CL. A prospective study of therapist facilitative interpersonal skills as a predictor of treatment outcome. J Consult Clin Psychol. 2016;84:57–66.

Røed K. Active social insurance. IZA J Labor Policy. 2012;1(1):8.

Landstad BJ, Wendelborg C, Hedlund M. Factors explaining return to work for long-term sick workers in Norway. Disabil Rehabil. 2009;31(15):1215–26.

Aasdahl L, Standal MI, Hagen R, Solbjør M, Bagøien G, Fossen H, Foldal VS, Bjørngaard JH, Rysstad T, Grotle M, Johnsen R. Effectiveness of’motivational interviewing’on sick leave: a randomized controlled trial in a social insurance setting. Scand J Work Env Hea. 2023;49(7):477.

Download references

Acknowledgements

We thank the caseworkers at NAV and the participants of the study.

Funding granted by The Research Council of Norway (Grant number: 256633). The funding organization had no role in the planning, execution or analyses of the study.

Open access funding provided by Norwegian University of Science and Technology

Author information

Authors and affiliations.

Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

Martin Inge Standal, Vegard Stolsmo Foldal, Lene Aasdahl, Egil A. Fors & Marit Solbjør

NTNU Social Research, Trondheim, Norway

Martin Inge Standal

Unicare Helsefort Rehabilitation Centre, Rissa, Norway

Lene Aasdahl

You can also search for this author in PubMed   Google Scholar

Contributions

MIS and VSF co-wrote the article. LA, EAF and MS contributed in the conception of the project. All authors designed the interview study. VSF analyzed and interpreted the data, and MIS, LA, EAF and MS contributed during the analysis process. The final categories were validated by all authors. VSF drafted the manuscript while MIS, LA, EAF and MS revised the manuscript. MIS finalized the article, and all authors revised the final version. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Martin Inge Standal .

Ethics declarations

Ethics approval and consent to participate.

The study was approved by the Regional Committees for Medical and Health Research Ethics in South East Norway (No: 2016/2300), and the trial was prospectively registered at clinicaltrials.gov NCT03212118 (registered July 11, 2017). The sick-listed workers were informed that the intervention was part of a research project and did not affect their rights or obligations as sick listed. Written informed consent was obtained from all participants prior to conducting interviews. The study was performed in accordance with the Declaration of Helsinki and the Guidelines by The Norwegian National Research Ethics Committee for medical and health research.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Standal, M.I., Foldal, V.S., Aasdahl, L. et al. Getting an outsider’s perspective - sick-listed workers’ experiences with early follow-up sessions in the return to work process: a qualitative interview study. BMC Health Serv Res 24 , 609 (2024). https://doi.org/10.1186/s12913-024-11007-x

Download citation

Received : 02 October 2023

Accepted : 18 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1186/s12913-024-11007-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Early follow-up
• Interview study
• Social insurance

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]