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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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What is Qualitative Research? Definition, Types, Methods, Examples and Best Practices

What is Qualitative Research?

Qualitative research is defined as a research method used to understand qualitative aspects of consumer or human behaviour and expectations, through open ended questions and answers. 

Unlike quantitative research that focuses on quantifiable numerical data derived from typically closed-ended questionnaires, qualitative research emphasizes on open-ended and conversational exploration and interpretation of subjective insights. This enables in-depth exploration as per the flow of the conversation, rather than being limited to specific questions and limited options to select responses. 

Qualitative research methodology involves collecting non-numeric data, such as those derived from interviews, observations, or open-ended questionnaire surveys, to gain a holistic understanding of a particular subject.

The emphasis is on capturing the richness and context of the participants’ perspectives, enabling a more nuanced comprehension of social phenomena. Researchers actively engage with participants, employing methods like interviews or focus groups to gather detailed information and delve into the underlying meanings and motivations behind behaviors.

Qualitative research analysis relies on qualitative data analysis techniques, which involve interpreting textual or visual data through coding, thematic analysis, or narrative exploration. This interpretive process aims to uncover underlying meanings and generate insights that contribute to a deeper understanding of the studied phenomena. Qualitative research is particularly valuable when exploring complex social issues, understanding holistic consumer perceptions on brands and products, cultural contexts, or individuals’ subjective experiences where quantitative methods may fall short in capturing the intricacies of expectations and behavior.

Characteristics of Qualitative Research

Here are the key characteristics of qualitative research:

• In-Depth Understanding: Qualitative research aims to provide a comprehensive and in-depth understanding of a particular phenomenon. Researchers delve into the context, meanings, and perspectives of the participants, allowing for a nuanced exploration of the subject.
• Flexible Study Designs: Unlike rigid experimental designs in quantitative research, qualitative studies often have flexible and evolving methodologies. Researchers may adapt their approach based on emerging insights, allowing for a more dynamic and responsive investigation.
• Subjectivity and Interpretation: Qualitative research recognizes the subjective nature of human experiences. Researchers actively engage with participants, and the interpretation of data involves the researcher’s subjective insights. This acknowledgment of subjectivity contributes to the richness of the findings.
• Non-Numeric Data Collection: Qualitative research primarily relies on non-numeric data, such as interviews, observations, or open-ended surveys. This approach enables the collection of detailed and context-rich information, emphasizing the quality and depth of data over numerical precision.
• Participant Perspectives: Qualitative researchers often seek to understand the world from the participants’ perspectives. This involves exploring individuals’ lived experiences, beliefs, and emotions, providing a more holistic view of the studied phenomenon.
• Emergent Design: Qualitative studies often have an emergent design, meaning that the research design and data collection methods may evolve during the course of the study. This adaptability allows researchers to explore unforeseen aspects and adjust their focus based on emerging patterns.
• Qualitative Data Analysis Techniques: Qualitative research involves unique data analysis techniques such as coding, thematic analysis, and narrative exploration. These methods help researchers identify patterns, themes, and meanings within the qualitative data collected.
• Contextualized Findings: Qualitative research emphasizes the importance of context in understanding behaviors and phenomena. Findings are often presented with detailed contextual information, providing a more holistic view of the studied subject within its natural setting.

These characteristics collectively contribute to the strength of qualitative research in exploring the complexity and depth of human experiences and social phenomena.

Key Components of Qualitative Research

The key components of qualitative research include:

• Research Design: This outlines the overall plan for the study, including the research questions or objectives, the chosen qualitative approach (e.g., phenomenology, grounded theory, ethnography), and the rationale for the selected methodology.
• Participants: Describes the individuals or groups involved in the study, including the criteria for selection. Qualitative research often involves purposeful sampling to ensure participants can provide rich and relevant information.
• Data Collection Methods: Specifies the techniques used to gather qualitative data. Common methods include interviews, focus groups, participant observations, and document analysis. Researchers choose methods based on the research questions and the nature of the phenomenon under investigation.
• Data Analysis Techniques: Details the approach to analyzing qualitative data. Techniques such as coding, thematic analysis, and constant comparison are employed to identify patterns, themes, and meanings within the collected data.
• Ethical Considerations: Addresses ethical issues and safeguards for participants. This includes obtaining informed consent, ensuring confidentiality, and minimizing any potential harm to participants throughout the research process.
• Researcher’s Role: Acknowledges the influence of the researcher in the study. This includes reflexivity—being aware of and transparent about the researcher’s biases, perspectives, and potential impact on the research.
• Validity and Reliability: While qualitative research doesn’t adhere to traditional notions of validity and reliability as in quantitative research, it emphasizes concepts like trustworthiness and credibility. Researchers employ various strategies, such as triangulation and member checking, to enhance the rigor of their findings.
• Results/Findings: Presents the outcomes of the data analysis, often organized around themes or patterns. Findings are typically illustrated with quotations or examples from participants to support the interpretation.
• Discussion and Interpretation: Involves a thorough examination and interpretation of the results in relation to existing literature and theoretical frameworks. Researchers discuss the implications of their findings and consider broader contexts.
• Conclusion: Summarizes the main insights, contributions, and potential avenues for future research. It provides a concise overview of the study’s significance and relevance in the broader academic or practical context.

These components work together to ensure a comprehensive and rigorous qualitative research study, allowing for a deep exploration of the complexities inherent in human experiences and social phenomena.

Types of Qualitative Research Methods with Examples

There are several types of qualitative research methods, each suited to different research questions and objectives. Here are some common types with examples:

• Definition: In-depth, one-on-one conversations between the researcher and the participant(s) to gather detailed information about their experiences, opinions, or perspectives.
• Example: Conducting interviews with survivors of a natural disaster to understand the psychological impact and coping strategies they employed.
• Definition: A group discussion led by a researcher to explore a specific topic, allowing participants to share their thoughts and engage in conversation with each other.
• Example: Using a focus group to gather insights from parents about their preferences and concerns regarding a new school curriculum.
• Definition: Systematic and careful observation of behavior, events, or phenomena in their natural setting, without intervention or manipulation by the researcher.
• Example: Observing and recording communication patterns in a workplace to understand team dynamics.
• Definition: An in-depth examination of a specific instance, situation, or individual, providing a detailed and holistic understanding of the subject.
• Example: Conducting a case study on a successful community health intervention program to identify key factors contributing to its effectiveness.
• Definition: Immersive research involving prolonged engagement and participation in the daily lives of a specific group or community to understand their culture and practices.
• Example: Living among and studying a nomadic tribe to document their traditions, social structures, and rituals.
• Definition: A method of developing theories by systematically gathering and analyzing data, allowing themes and concepts to emerge directly from the data.
• Example: Using grounded theory to explore the process of decision-making in a business organization without preconceived notions.
• Definition: Systematic analysis of textual, visual, or audio content to identify patterns, themes, and meanings.
• Example: Analyzing online forum discussions to understand public sentiment and concerns about a controversial policy.
• Definition: Exploration of individual or collective stories to understand the meaning and significance of experiences.
• Example: Collecting and analyzing personal narratives of cancer survivors to uncover common themes and coping strategies.
• Definition: A philosophical approach and research method focused on exploring and describing lived experiences from the perspective of the individuals who have had them.
• Example: Studying the phenomenon of “flow” by exploring the subjective experiences of individuals deeply engaged in challenging activities like sports or creative endeavors.

Benefits of Qualitative Research

Qualitative research offers several benefits, including:

• In-Depth Understanding: Qualitative research allows researchers to explore complex phenomena in-depth, providing a rich and nuanced understanding of the subject matter. It goes beyond surface-level insights, capturing the depth and context of human experiences.
• Flexibility: Qualitative methods are flexible and adaptable, allowing researchers to adjust their approach based on emerging insights. This flexibility is particularly valuable when exploring dynamic or unexpected aspects of a phenomenon.
• Contextual Insight: By emphasizing the context in which behaviors or phenomena occur, qualitative research provides a holistic view. This contextual insight is crucial for understanding the cultural, social, or environmental factors influencing the subject of study.
• Participant Perspectives: Qualitative research actively involves participants, allowing them to share their perspectives, experiences, and voices. This participant-centered approach contributes to a more authentic and representative portrayal of the studied phenomenon.
• Exploratory Nature: Qualitative research is well-suited for exploratory studies where the goal is to generate hypotheses, theories, or a deeper understanding of a topic. It helps researchers uncover new insights and explore uncharted territory.
• Applicability to Complex Social Issues: Qualitative research is particularly effective in studying complex social issues, diverse cultures, and subjective experiences. It enables researchers to navigate and make sense of intricate social dynamics.
• Cultural Sensitivity: Qualitative methods allow for cultural sensitivity and the exploration of cultural nuances. Researchers can adapt their approach to different cultural contexts, ensuring that the study is respectful and relevant to the participants.
• Naturalistic Settings: Qualitative research often takes place in naturalistic settings, providing a realistic and ecologically valid environment for studying behaviors. This setting enhances the ecological validity of the findings.
• Theory Development: Qualitative research contributes to the development and refinement of theories. Through inductive reasoning, researchers can generate new concepts and theoretical frameworks based on the patterns and themes identified in the data.
• Humanizing Data: Qualitative research humanizes data by bringing personal stories and experiences to the forefront. This approach fosters empathy and a deeper connection with the subjects under investigation.
• Validity and Trustworthiness: While qualitative research doesn’t strictly adhere to traditional notions of validity, it emphasizes trustworthiness through strategies like triangulation, member checking, and prolonged engagement, enhancing the credibility of the findings.

These benefits make qualitative research a valuable approach for exploring the complexities of human behavior, attitudes, and social phenomena.

Potential Challenges of Qualitative Research

Qualitative research comes with its own set of challenges, including:

• Subjectivity and Bias: The researcher’s subjectivity and biases can influence the study, from data collection to analysis and interpretation. Maintaining objectivity can be challenging, and researchers must be aware of their own perspectives.
• Limited Generalizability: Findings from qualitative research are often context-specific and may not be easily generalizable to broader populations. The emphasis on depth can sometimes limit the applicability of the results beyond the studied group.
• Data Interpretation Complexity: Analyzing qualitative data can be complex and subjective. Different researchers may interpret the same data differently, leading to potential variations in findings.
• Resource Intensiveness: Qualitative research can be time-consuming and resource-intensive. Conducting interviews, transcribing data, and analyzing rich textual information can require a significant investment of time and effort.
• Small Sample Sizes: While qualitative research allows for in-depth exploration, it may raise questions about the representativeness of findings.
• Ethical Challenges: Dealing with ethical considerations, such as ensuring informed consent, maintaining confidentiality, and minimizing harm, can be intricate, especially when studying sensitive topics or vulnerable populations.
• Validity and Reliability Concerns: Traditional notions of validity and reliability may not apply directly to qualitative research. Establishing the trustworthiness of findings involves alternative strategies like triangulation, member checking, and peer review.
• Difficulty in Replication: Due to the unique nature of qualitative studies and the importance of context, replication of findings can be challenging. Other researchers may find it difficult to recreate the exact conditions or interpret data in the same way.
• Risk of Misinterpretation: Misinterpreting participant responses or cultural nuances is a risk in qualitative research. Careful attention to language, context, and cultural sensitivity is essential to minimize misinterpretation.
• Overemphasis on Verbal Data: Qualitative research often relies on verbal data, potentially neglecting non-verbal cues. This limitation might hinder a comprehensive understanding of participants’ experiences.
• Limited Quantification: Qualitative data is predominantly non-numeric, making it challenging to quantify and measure the extent or frequency of specific phenomena. This can limit the ability to make statistical comparisons.

Understanding these challenges helps researchers navigate the complexities of qualitative research and enhances the rigor and credibility of their studies.

Best Practices for Qualitative Research in 2024

To ensure the rigor and credibility of qualitative research, consider the following best practices:

• Clearly Define Research Questions: Clearly articulate your research questions or objectives to guide the study. This clarity helps maintain focus and ensures that data collection and analysis align with the research goals.
• Choose Appropriate Methods: Select qualitative research methods that align with your research questions. Consider the strengths and limitations of each method, such as interviews, focus groups, or observations, and choose the most suitable approach for your study.
• Pilot Test Data Collection Instruments: Before full-scale data collection, conduct a pilot test of your interview guides, surveys, or observation protocols. This helps identify potential issues, refine questions, and ensure the instruments are effective.
• Establish Trust with Participants: Build rapport and trust with participants to encourage open and honest responses. Clearly communicate the purpose of the study, assure confidentiality, and obtain informed consent.
• Use Purposive Sampling: Select participants purposefully based on criteria relevant to your research questions. This approach ensures that participants have valuable insights related to the study’s objectives.
• Record and Transcribe Interviews: Record interviews (with participant consent) to capture nuances and details accurately. Transcribe the recordings verbatim to facilitate thorough data analysis.
• Maintain Reflexivity: Acknowledge and reflect on your own biases, values, and perspectives throughout the research process. Reflexivity enhances transparency and helps mitigate the impact of the researcher’s subjectivity.
• Ensure Data Saturation: Continue data collection until data saturation is achieved—meaning that new information ceases to emerge. Saturation ensures that the study comprehensively explores the research questions.
• Thorough Data Analysis: Use rigorous and systematic data analysis techniques, such as coding, thematic analysis, or grounded theory, to derive meaningful insights from the collected data. Maintain transparency in the analytical process.
• Member Checking: Validate findings with participants through member checking. Share preliminary results or interpretations with participants to ensure accuracy and gain their perspectives on the findings.
• Triangulation: Use multiple data sources, methods, or researchers to enhance the validity and reliability of findings. Triangulation helps corroborate results and provides a more robust understanding of the phenomenon.
• Maintain Ethical Standards: Adhere to ethical guidelines throughout the research process. Prioritize informed consent, protect participant confidentiality, and consider the potential impact of the research on participants.
• Document Decision-Making Processes: Keep detailed records of decisions made during the research, such as changes in the research design or data analysis approach. This documentation enhances transparency and replicability.
• Peer Review: Seek feedback from colleagues or experts in qualitative research to validate your study’s rigor. Peer review provides an external perspective and helps identify potential biases or oversights.

By following these best practices, qualitative researchers can enhance the quality, reliability, and validity of their studies, ultimately contributing to a more robust understanding of the researched phenomena.

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Qualitative Research Definition

Qualitative research methods and examples, advantages and disadvantages of qualitative approaches, qualitative vs. quantitative research, showing qualitative research skills on resumes, what is qualitative research methods and examples.

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Table of Contents

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

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

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

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

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Who Uses Qualitative Research?

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

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

Other careers that may involve qualitative research include: 

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

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

Grounded Theory

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

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

Action Research

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

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

>>MORE: Explore how action research helps consultants serve clients with Accenture’s Client Research and Problem Identification job simulation .

Phenomenological Research

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

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

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Ethnography

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

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

Case Studies

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

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

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

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

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

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

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

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

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

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

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

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• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

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

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

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

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

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

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

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

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

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

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

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Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

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Unit 6: Qual vs Quant.

28 Qualitative Methods in Communication Research

Qualitative methods in communication research.

In communication research, both quantitative and qualitative methods are essential for understanding different aspects of communication processes and effects. Here’s how qual methods can be applied:

• Conducting in-depth interviews to explore individuals’ experiences and perceptions of their interpersonal relationships
• Conducting in-depth interviews with individuals to explore their experiences, opinions, and feelings about communication topics.
• Facilitating group discussions to gather diverse perspectives on communication issues within relationships.
• Facilitating group discussions to gather diverse perspectives on communication issues or media content.
• Observing and documenting communication practices within specific social or cultural groups to understand their norms and behaviors.
• Observing and documenting communication practices within specific cultural or social groups to understand their communication norms and behaviors.
• Thematic Analysis : Analyzing qualitative data from interviews, focus groups, or media content to identify recurring themes and patterns, for example, patterns in interpersonal communication and relationships.

Communication Research in Real Life Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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Mixed Feelings and Mixed Methods in Psychological Science

• Conducting Research

Essential Science Conversations

February 2022

• Transcript (PDF, 170KB)
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In this essential science conversation, expert panelists explore how qualitative and quantitative research approaches can be used as complementary tools, each with specific advantages and limitations, that have evolved to meet emerging research needs.

This program does not offer CE credit.

Elizabeth G. Creamer, EdD

Professor emerita, Virginia Tech.

Joseph P. Gone, PhD

Harvard University.

Eric A. Youngstrom, PhD

Professor of psychology and neuroscience, and psychiatry at University of North Carolina at Chapel Hill.

Mitch Prinstein, PhD

Chief Science Officer, APA.

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Open Access

Peer-reviewed

Research Article

From many voices, one question: Community co-design of a population-based qualitative cancer research study

Roles Conceptualization, Data curation, Formal analysis, Project administration, Writing – original draft

* E-mail: [email protected]

Affiliations Cancer Council Queensland, Brisbane, Queensland, Australia, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Queensland, Australia

Roles Conceptualization, Data curation, Formal analysis, Project administration, Writing – original draft, Writing – review & editing

Affiliations Cancer Council Queensland, Brisbane, Queensland, Australia, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Queensland, Australia, Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

Roles Conceptualization, Data curation, Formal analysis, Project administration, Writing – review & editing

Affiliation Cancer Council Queensland, Brisbane, Queensland, Australia

Roles Conceptualization, Writing – review & editing

Affiliations Psychedelic Medicine and Supportive Care Lab, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, School of Psychology, The University of Queensland, Brisbane, Queensland, Australia, School of Nursing, Queensland University of Technology, Brisbane, Queensland, Australia

Affiliations Centre for Health Services Research, The University of Queensland, Brisbane, Queensland, Australia, Dalla Lana School of Public Health, The University of Toronto, Toronto, Ontario, Canada

Roles Conceptualization

Affiliations Icon Cancer Centre, Brisbane, Queensland, Australia, Princess Alexandra Hospital, Brisbane, Queensland, Australia, School of Medicine, University of Queensland, Brisbane, Australia

Affiliation Toowoomba Base Hospital, Toowoomba, Queensland, Australia

Affiliation Cancer Alliance Queensland, Brisbane, Queensland, Australia

Affiliations Cancer Council Queensland, Brisbane, Queensland, Australia, Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia, School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia

• Susannah K. Ayre, 
• Elizabeth A. Johnston, 
• Xanthia E. Bourdaniotis, 
• Leah Zajdlewicz, 
• Vanessa L. Beesley, 
• Jason D. Pole, 
• Aaron Hansen, 
• Harry Gasper, 
• Danica Cossio, 

• Published: August 26, 2024
• https://doi.org/10.1371/journal.pone.0309361
• Reader Comments

This study formed the development stage of a population-based survey aiming to: (i) understand the needs and experiences of people affected by cancer in Queensland, Australia and (ii) recruit a pool of participants for ongoing cancer survivorship research. The current study aimed to co-design and test a single qualitative survey question and study invitation materials to maximise acceptability of, and participation in, the survey and future research.

Fifty-two community members, including cancer survivors and caregivers, participated across 15 co-design workshops and 20 pretest interviews. During workshops, participants generated and refined ideas for an open-ended survey question and provided feedback on a study invitation letter. The use of a single, open-ended question aims to minimise participant burden while collecting rich information about needs and experiences. The research team then shortlisted the question ideas and revised study invitation materials based on workshop feedback. Next, using interviews, community members were asked to respond to a shortlisted question to test its interpretability and relevance and to review revised invitation materials. Content analysis of participant feedback was used to identify principles for designing study materials.

Principles for designing qualitative survey questions were identified from participant feedback, including define the question timeframe and scope; provide reassurance that responses are valid and valued; and use simple wording . Principles for designing study invitation materials were also identified, including communicate empathy and sensitivity; facilitate reciprocal benefit; and include a ‘human element’ . The qualitative survey question and study invitation materials created using these principles were considered relevant and acceptable for use in a population-based survey.

Conclusions

Through community consultation and co-design, this study identified principles for designing qualitative data collection and invitation materials for use in cancer survivorship research. These principles can be applied by other researchers to develop study materials that are sensitive to the needs and preferences of community members.

Citation: Ayre SK, Johnston EA, Bourdaniotis XE, Zajdlewicz L, Beesley VL, Pole JD, et al. (2024) From many voices, one question: Community co-design of a population-based qualitative cancer research study. PLoS ONE 19(8): e0309361. https://doi.org/10.1371/journal.pone.0309361

Editor: Md. Shahjalal, North South University, BANGLADESH

Received: April 28, 2024; Accepted: August 11, 2024; Published: August 26, 2024

Copyright: © 2024 Ayre et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: As per our Participant Information and Consent Form (PICF), participants consented to their interview responses being kept confidential unless required by law, and their data being stored in a secure, password-protected folder that was only accessible to members of the research team. As per our approved ethics protocol (University of Southern Queensland Human Research Ethics Committee Approval No.: ETH2023-0140), it is also a requirement that no research data are made available or shared via open access, restricted access, mediated access, or as metadata only. This restriction is necessary due to the collection of potentially identifiable and sensitive data in our workshops and interviews, where participants shared their personal experiences of cancer. Sharing excerpts of transcripts on a public repository would therefore violate the agreement to which the participants consented. However, a large number of de-identified excerpts of the transcripts have been shared within the paper (see Tables 3 and 5 ). Requests for data will also be considered by the Ethics Committee and Principal Investigator (BG) and if approved, non-identifiable, truncated, and/or aggregated data may be shared with the third-party for a specific purpose such as re-analysis. To request access, please contact the University of Southern Queensland Human Research Ethics Committee via [email protected] .

Funding: The work was supported by Cancer Council Queensland. Researchers affiliated with Cancer Council Queensland (SA, EJ, XB, LZ, GL, BG) were involved in the study design, data collection and analysis, decision to publish, and preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In recent decades, an ageing population has resulted in more people being diagnosed with cancer, and improvements in cancer detection and treatment means that people are living longer post-diagnosis [ 1 , 2 ]. While people diagnosed with cancer have unique disease trajectories, a common experience of cancer and its treatment is the widespread impact on a person’s health and wellbeing, often continuing well after treatment completion [ 3 ]. Informal caregivers (i.e., family and friends) are closely involved in supporting their loved ones to manage the impact of cancer and its treatment, often with minimal preparation for their caregiving role [ 4 ]. Consequently, caregivers can experience poor health and wellbeing due to the prolonged stress and physical demands involved [ 5 ]. In the context of increasingly resource-constrained healthcare systems, there is a growing need for community-based supportive care services that are effective in supporting the increasing number of cancer survivors and caregivers [ 6 ].

The delivery of effective supportive care services cannot be achieved without a comprehensive understanding of the needs and experiences of the cancer survivors and caregivers for whom these services are provided to. Evidence indicates that patient- and family-centred interventions result in higher satisfaction with healthcare, increased knowledge and skills for managing self-care behaviours, reduced reliance on healthcare services, and improved quality of life for both patients and caregivers [ 7 ]. Many instruments have been used in research and clinical practice to assess needs [ 8 , 9 ]. The Supportive Care Needs Survey-Short Form (SCNS-SF34) [ 10 ], Cancer Survivors’ Unmet Needs (CaSUN) [ 11 ], Comprehensive Needs Assessment Tool for Cancer Caregivers (CNAT-C) [ 12 ], and Supportive Care Needs Survey for partners and caregivers (SCNS-P&C) [ 13 ] are among the most widely used. However, such instruments rely on set items with pre-determined responses that may not capture the full scope of needs and do not allow respondents to express their experiences in their own words [ 14 ]. Thus, qualitative survey methods are necessary to achieve a comprehensive understanding of the needs and experiences of people affected by cancer at the population level. To date, no suitable qualitative survey exists, providing an opportunity for community participation in co-designing materials to capture the supportive care needs and experiences of people affected by cancer.

Co-design research methods encompass various levels of consumer engagement in the research process, including participation in study conceptualisation, design, conduct, and reporting [ 15 ]. Consumer involvement in research can contribute to better study outcomes, including higher enrolment and retention rates [ 16 ]. Additionally, thorough testing of study materials with consumers is recommended to ensure materials are easy to understand, sensitively worded, and able to elicit meaningful data to address research aims [ 17 ].

Through active and repeated engagement with community members, this study aimed to: (i) develop and test a qualitative survey question for collecting rich information about the supportive care needs and experiences of people affected by cancer, and (ii) design study invitation materials that are relevant and acceptable to cancer survivors and their caregivers. The use of a single, open-ended question aims to minimise participant burden and maintain a broad investigation of supportive care needs, rather than the traditional itemised approach. The materials developed in this study will be used in a new population-based study for understanding the needs and experiences of adults affected by cancer in Queensland, Australia and establishing a research-ready pool of participants to take part in ongoing cancer survivorship research. Findings from the current study will provide principles for researchers to apply when designing qualitative data collection tools and study invitation materials for research into supportive care needs and experiences, particularly in the cancer context.

Materials and methods

This study comprised two phases of qualitative research: 1) co-design workshops and 2) interviews . As outlined in Fig 1 , both phases included community members and the research team working collaboratively to co-design a qualitative survey question and study invitation materials. Ethical approval for this study was obtained from the University of Southern Queensland Human Research Ethics Committee (ref: ETH2023-0140). This study is reported as per the Consolidated Criteria for Reporting Qualitative Research (COREQ) Checklist [ 18 ] (see S1 Table ).

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https://doi.org/10.1371/journal.pone.0309361.g001

Participants and recruitment

This study was conducted in Queensland, Australia. Queensland has almost one third of its population living outside of major cities [ 19 ], which presents several challenges to accessing cancer treatment and support due to the greater travel distances and costs involved [ 20 ]. For both the co-design workshops and interviews, eligible participants were aged 18 years or older and English-speaking. Additional eligibility criteria for interview participants included a personal experience with cancer, either as a survivor or caregiver. Digital and printed recruitment flyers were distributed via networks associated with Cancer Council Queensland or the broader research team between 11 October 2023 and 14 February 2024. To support the recruitment of priority populations, such as culturally and/or linguistically diverse (CALD) groups, the research team submitted study information to a health consumers network [ 21 ] for inclusion in their e-newsletter. As data collection advanced, recruitment was supplemented through snowball sampling, with workshop participants invited to share recruitment flyers with friends, family members, and colleagues.

Prospective participants self-enrolled into a workshop and/or interview via an online participant information and consent form administered through REDCap (hosted by the University of Southern Queensland) [ 22 , 23 ]. Consenting participants were then contacted by telephone and/or email to arrange a time for their participation. For the interviews, new participants were recruited alongside eligible workshop participants to obtain feedback from people with and without prior knowledge of the study.

Recruitment for the co-design workshops and interviews continued until a diverse sample had been achieved and the research questions had been adequately explored, determined by the authors through concurrent data collection and analysis. Due to the large number of online registrations for the interviews, new and existing participants were purposively sampled based on their demographic characteristics, including gender, ethnicity, Indigenous status, and geographical location, as well as their experience with cancer (i.e., survivor or caregiver) to ensure that diverse perspectives were represented [ 24 ]. To acknowledge their contributions to the study, workshop and interview participants received a voucher valued at AU$100.00 (approx. 120 minutes) and AU$50.00 (approx. 60 minutes), respectively.

Data collection

Phase 1: co-design workshops..

Each workshop included two to four participants. As participants were grouped based on convenience, each workshop included a combination of cancer survivors, caregivers, and/or other community members. The workshops were facilitated by two female researchers with undergraduate or postgraduate degrees in health science fields and training in qualitative data collection (SA, XB, and/or EJ). The facilitators had no prior relationship with the participants. At the start of each workshop, the facilitators introduced themselves, including their role in the research team and their academic background. Workshops were conducted as either online ( n = 9), in-person ( n = 1), or hybrid (i.e., online and in-person) ( n = 5) sessions using Microsoft Teams. In-person participants attended the session at one of two not-for-profit organisations, where participants were provided with the relevant materials (e.g., pen, paper). Participants attending online were asked to source these materials themselves. Workshops were audio-recorded and transcribed via Microsoft Teams. The workshops comprised several activities, with accompanying information and instructions provided on presentation slides. An overview of the workshop protocol is available in S2 Table .

The first activity in the workshop used the nominal group technique [ 25 ] to generate and refine a pool of qualitative questions that could be used in the population-based survey for understanding the supportive care needs and experiences of people affected by cancer. This technique fosters balanced participation, serving as an effective and efficient method for achieving group consensus [ 25 ]. First, participants were asked to individually brainstorm ideas for how to word the qualitative question. A preamble to the question drafted by the research team was shared with participants as a prompt for question generation (see S2 Table for further details). Participants were instructed that this question should be a standalone, broad, open-ended question that allows respondents to describe their needs and experiences using their own words, with the purpose of generating data that would inform service delivery at Cancer Council Queensland. Second, participants shared their questions with the group following a ‘round-robin’ process, during which a facilitator typed the questions verbatim onto the presentation slides. Third, participants collectively reflected on the proposed questions, sought clarification from one another, and adjusted wording as needed (e.g., removing potentially insensitive words). Finally, participants privately voted on their two most preferred questions using an online or paper-based poll. Votes were tallied to identify questions with the highest scores which were then presented to the group for further discussion and refinement, resulting in a total of one to five questions per group.

The second activity in the workshop involved participants providing feedback on the wording and format of an invitation letter for the population-based survey. This letter was drafted by the research team based on an example from a previous registry study. It comprised a single page of information about the research project, including its aims, instructions on how to participate, and permissions to recruit participants via the registry. During the workshops, participants were also prompted to discuss an appropriate time post-diagnosis to invite individuals to complete the survey.

Key principles endorsed by workshop participants for developing a qualitative survey question were presented to the investigator team, alongside questions that were open-ended, broad in scope, and aligned with these principles (see Data analysis section for methods used to identify key principles). The investigator team, comprising clinicians, researchers, and academics with expertise in cancer survivorship, supportive care, medical oncology, behavioural science, and digital health ( n = 7; see S3 Table ), were asked to rank their five most preferred questions (1 = ‘most preferred’, 5 = ‘least preferred’) via an anonymous online survey in REDCap [ 22 , 23 ]. Four of the highest ranked questions were shortlisted for testing during interviews. Study invitation materials were also revised based on key principles endorsed by participants in the workshops.

Phase 2: Interviews.

Online semi-structured interviews were conducted, audio-recorded, and transcribed using Microsoft Teams. Each interview was facilitated by one researcher (SA or XB). The interviews served as an opportunity for member checking of findings from the workshops. A summary of the interview protocol is available in S4 Table . Participants firstly reviewed the revised invitation materials and provided feedback on their readability and design. They were also asked to discuss an appropriate time for sending individuals a reminder letter to complete the survey. Participants were then randomly presented with one of the four shortlisted questions and given five uninterrupted minutes to respond to and submit their written response via the online chat function. Drawing on principles of the ‘think aloud’ method [ 26 ], participants then verbalised their thoughts, assumptions, and decisions while reading, interpreting, and responding to the question. Scripted and spontaneous probes were used to clarify interpretation as needed. Participants were then presented with the three alternative questions and asked to nominate their preferred question based on interpretability and relevance. After 14 interviews, there was a clear consensus on the most appropriate question to include in the survey ( n = 13 votes). An additional six interviews were then conducted, focusing solely on this question. The interviews used the ‘think aloud’ method [ 26 ] to finalise the wording of this question and confirm its interpretability and relevance. Following the 20 interviews, feedback on the revised study invitation materials was applied by the research team if considered feasible and relevant to the research aims.

Demographic survey.

Age, gender, country of birth, language spoken at home, ethnicity, postcode of residential address, and personal history with cancer (including patient or caregiver status) were collected via an online survey in REDCap [ 22 , 23 ] at the start of each co-design workshop. For participants who did not attend a workshop, demographic data were collected through structured questions at the end of their interview.

Data analysis

Descriptive statistics were used to summarise participant characteristics for the co-design workshops and interviews. Transcripts generated via Microsoft Teams were reviewed for accuracy alongside the audio recordings. Transcripts were then analysed using content analysis to identify key principles endorsed by participants for developing a qualitative survey question and designing study invitation materials. Content analysis involves the systematic coding of text into categories based on the words and language used, centring participants’ voice in the analysis [ 27 ]. Each transcript was coded by one author (XB, SA, or EJ), with decisions regularly discussed with other authors and documented using an audit trail.

In total, 15 co-design workshops with 44 participants and 20 interviews were completed (see Fig 2 ). Twelve of the 20 interview participants had also completed a co-design workshop. The characteristics of participants in the two phases of consumer consultation are summarised in Table 1 . Both phases included representation from population subgroups, including 5–7% who identified as Aboriginal and/or Torres Strait Islander, 9–15% who used English as a second language, 20% who were born overseas, and 27–30% who lived in a rural area.

a Invalid responses were identified based on a combination of factors (e.g., duplicated IP addresses with different names, invalid postcodes or phone numbers, replicated responses in a short period of time, unusual completion times). Where necessary, responses flagged as potentially invalid were investigated further through phone and/or email contact. b Individuals were purposively selected to achieve maximum variation in the age, gender, ethnicity, and geographic location of participants. c 12 of the 20 participants in the interviews also participated in a co-design workshop.

https://doi.org/10.1371/journal.pone.0309361.g002

https://doi.org/10.1371/journal.pone.0309361.t001

Qualitative survey question

Analysis of the 15 co-design workshop responses yielded 16 eligible questions (see Table 2 ). Additionally, 10 principles were identified from participant feedback during the workshops for developing qualitative data collection tools (see Table 3 ). These principles were to: avoid assumptions and leading questions; define the question timeframe and scope; directly address the respondent; foster a collectivist perspective; gather experiential data from respondents for researchers to identify solutions; prompt an open-minded response; provide reassurance that responses are valid and valued; use an engaging design and accessible formatting (e.g., large and easy to read text) ; use sensitive language; and use simple wording .

https://doi.org/10.1371/journal.pone.0309361.t002

https://doi.org/10.1371/journal.pone.0309361.t003

A flowchart showing idea generation and shortlisting for the qualitative survey question is presented in Fig 3 . During the workshops, participants generated a total of 173 questions with 42 questions selected through participant voting. Of these 42 questions, 14 (33%) did not meet pre-defined criteria (i.e., not open-ended, not broad in scope) and 1 question was a duplicate, warranting the exclusion of these questions. An additional 11 (26%) questions were excluded for not aligning with the principles endorsed by workshop participants. Although participants advocated for including a timeframe within the question, a limited number of questions aligned with this principle. Rather than excluding these questions, a timeframe was added as needed (e.g., ‘right now’ was added to Question 1; see Table 2 ). Similarly, few questions complied with the principles of prompting an open-minded response (e.g., ‘in an ideal world’) and fostering a collectivist perspective (e.g., ‘you or those you care for’). Questions that did not align with these principles were not excluded as the population-based study aimed to capture actual needs and experiences from the perspective of individual respondents.

a i.e., Terms such as ‘quality of life’ and ‘daily living’. b i.e., Terms such as ‘cancer journey’. c i.e., Questions that imply a negative experience, such as ‘challenging’. d i.e., Questions such as ‘What would make life easier for you?’.

https://doi.org/10.1371/journal.pone.0309361.g003

From the 16 eligible questions, preferential voting by the investigator team resulted in a shortlist of 4 questions. In line with principles endorsed by workshop participants, team members were in favour of including prompts alongside the question, with the most popular prompts being: ‘Share as much detail as you would like to’ ( n = 5, 71%) and ‘For example, you might like to consider your practical, emotional, psychological, financial, relational, and cultural needs’ ( n = 5, 71%). Table 4 summarises participants’ interpretations of and feedback on these questions and prompts in the interviews. Participants’ responses to the shortlisted questions are provided in S5 Table for comparison.

https://doi.org/10.1371/journal.pone.0309361.t004

Of the four shortlisted questions, interview participants preferred the two-part questions that explicitly asked about needs and experiences (i.e., Options 1 and 2) (see Table 4 ). Participants thought this question structure would generate richer information, particularly from respondents who find it difficult to articulate their needs. Of these two options, most participants ( n = 13/14) endorsed Option 2 as it was easier to read and interpret, applicable to both cancer survivors and their caregivers, and relevant to any timepoint post-diagnosis. Participants recommended clarifying the timeframe that ‘right now’ referred to. For the question prompts, it was suggested to substitute complex words (i.e., replace ‘relational’ with ‘social’), remove words with similar meanings (i.e., remove ‘psychological’ and retain ‘emotional’), and broaden the scope (i.e., add ‘physical’ and ‘spiritual’). Option 2 was revised accordingly, with the wording of this question also changed to present tense to provide further clarification of the timeframe. These revisions resulted in the final question:

Thinking about the past month, how is cancer affecting your life and what do you need? Share as much detail as you would like to. For example, you might like to consider your physical, emotional, practical, financial, social, cultural, and spiritual needs, or any other needs you might have.

In the final six interviews, this question demonstrated capacity to elicit relevant, detailed data on the unmet supportive care needs and experiences of people affected by cancer (see S5 Table ).

Study invitation materials

Ten principles for designing study invitation materials were identified from participant feedback during the co-design workshops and validated during the interviews. These principles are presented in Table 5 alongside an explanation and participant quotes. These principles were to: communicate empathy and sensitivity; consider appropriate timing; convey credibility and legitimacy; facilitate reciprocal benefit; include a ‘human element’; increase accessibility and ease of participation; optimise readability; promote inclusivity; provide reassurance around privacy; motivate and incentivise participation; and support informed decisions . An eleventh principle, promote inclusivity , emerged from the interview data only (see Table 5 ). Table 5 also provides examples of how each principle was applied in this study to develop invitation materials for the proposed population-based survey. For example, following the co-design workshops, a flyer was created to accompany the letter, which included a personal quote from a cancer survivor emphasising the value of the research for people impacted by cancer.

https://doi.org/10.1371/journal.pone.0309361.t005

The revised invitation materials presented to participants in the 20 interviews were highly accepted, with 19 (95%) participants indicating they would likely respond to the qualitative survey question. The one participant who indicated they would not complete the survey was reluctant to scan a quick response (QR) code. Therefore, instructions on alternative methods for completing the survey were emphasised on the invitation materials in the final revision stage, alongside several other minor changes, such as increasing the font size and simplifying the language used.

This qualitative study used co-design methods to develop and test study materials for capturing the supportive care needs and experiences of cancer survivors and their caregivers in a future population-based survey. Principles for designing qualitative data collection tools and study invitation materials were identified in workshops with community members and validated in interviews. These principles can be used more broadly by health and survivorship researchers to design study materials that align with community preferences.

To date, studies examining strategies to optimise participation in cancer research have focused primarily on methods of advertisement (e.g., social media, text messages), incentives, eligibility criteria, and outcome measures [ 28 , 29 ]. However, few studies have investigated community preferences for invitation materials. A conceptual model developed by Chhatre and colleagues [ 30 ] describes strategies for recruiting cancer patients into clinical trials using a patient-centred approach. Recommended strategies mapped onto four concepts, including trust, communication, expectations, and attitudes, and ranged from protecting patient health information to emphasising the altruistic value of research involvement [ 30 ]. These findings align with principles endorsed by community members in the current study, which supports the applicability of these principles across clinical and observational research settings.

To the authors’ knowledge, few studies have reported on the development and testing of qualitative surveys for health research, despite the importance of question wording in survey-based studies [ 31 ]. Principles identified in the current study both validate and expand on those previously identified in best practice guidelines for qualitative research [ 31 – 33 ]. For example, similar to consumers in this study, Braun and colleagues [ 31 ] recommend that survey questions are short and unambiguous, void of assumptions, and include examples to guide the scope of responses. Similar to other widely used surveys [ 10 – 12 ], a timeframe was also added to the question (i.e., the past month) to minimise participant burden when responding, enable repeated data collection over time, and allow comparability of responses by time since diagnosis. The current study also identified additional principles for designing a qualitative survey. For example, leveraging the expertise of respondents by asking them to share their experiences, rather than just their needs, enables researchers to identify trends and gaps in care at the population-level. Considering that the principles in this study were endorsed by a diverse sample of cancer survivors and caregivers, the principles identified may also be applicable to studies investigating the supportive care needs and experiences of people affected by other health conditions.

The single, open-ended survey question designed in the current study was developed to collect rich, detailed information on the supportive care needs and experiences of people affected by cancer for the purpose of informing service delivery. Current literature on supportive care needs is largely based on data derived from quantitative measures. These measures are confined to specific supportive care domains, with needs in the cognitive, spiritual, sexual, or financial domains often overlooked or excluded from shorter versions of tools [ 14 , 34 ]. In contrast, the question developed in this study asks about supportive care needs in the context of participants’ experiences (i.e., how cancer is affecting their life). Based on participant feedback in the workshops, the question includes a prompt that lists various domains of potential need. When this prompt was tested in interviews, its inclusion did not appear to constrain or direct participant responses. Therefore, the question provides an opportunity for respondents to share their experiences and to define what is important to them. The final question demonstrated applicability and acceptability among a diverse sample of cancer survivors and caregivers. Thus, the question presents an acceptable and potentially effective method for assessing the supportive care needs and experiences of people affected by cancer.

In the current study, there were some divergent views among community members regarding how to design a qualitative survey question. For example, some participants suggested asking about supportive care needs in an open-minded manner, using phrases such as ‘in an ideal world’ and ‘what support do you wish you had’. However, this principle conflicted with other participants’ suggestions to define the scope of the question by providing realistic examples of what supportive care services may be provided, to manage expectations of the research outcomes. Similarly, some participants noted that using terms like ‘ideal’ could be insensitive as they may imply a sense of choice or control in a person’s cancer trajectory. Therefore, the principle of asking about needs in an open-minded manner was not applied in the current study but may be suitable to other populations or contexts depending on the research aims and scope.

Strengths and limitations

This study used an iterative qualitative design, facilitating active and repeated engagement of community members in developing and testing study materials. Including a subset of participants in both phases of consumer consultation enabled the principles identified in workshops to be validated through member checking in interviews [ 24 ]. The use of two different consultation methods was another key strength; the workshops fostered collaborative group discussion which supported idea generation, while the interviews provided a platform for in-depth exploration of individual perspectives and experiences [ 24 ]. Finally, the study included a diverse sample, involving people who were born overseas, used English as a second language, identified as Aboriginal and/or Torres Strait Islander, and lived in a rural area.

The main limitation of this study is the potential for self-selection bias. The consumer consultations relied on individuals agreeing to participate in a research study and being able to speak English. Given that an estimated 900,000 people in Australia have low proficiency in spoken English [ 35 ], and that CALD groups experience poorer health outcomes compared to the general population [ 36 ], future research should work with non-English speaking people to design study materials that facilitate their participation in cancer research [ 37 ]. Additionally, the current study did not collect data on other characteristics, such as participants’ sexuality, education, and income. It is therefore unknown whether the findings represent diversity within these groups.

Through active and repeated consultation with community members, this study identified principles for designing qualitative data collection tools and study invitation materials for use in cancer survivorship research. These principles were used to design and test an open-ended survey question and study invitation materials for use in a population-based study of the supportive care needs and experiences of cancer survivors and their caregivers. These principles can also be used by other researchers to optimise community participation in their qualitative research and to inform support service providers about the needs and experiences of consumers.

Supporting information

S1 table. completed consolidated criteria for reporting qualitative research (coreq) checklist..

https://doi.org/10.1371/journal.pone.0309361.s001

S2 Table. Overview of protocol for co-design workshops.

https://doi.org/10.1371/journal.pone.0309361.s002

S3 Table. Expertise of the co-investigator research team who voted on the questions generated from the co-design workshops for inclusion in the interviews.

https://doi.org/10.1371/journal.pone.0309361.s003

S4 Table. Overview of protocol for interviews.

https://doi.org/10.1371/journal.pone.0309361.s004

S5 Table. Responses to the shortlisted and final question in the interviews.

https://doi.org/10.1371/journal.pone.0309361.s005

Acknowledgments

The authors thank all individuals who participated in a workshop and/or interview.

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• Open access
• Published: 25 August 2024

Navigating sexual minority identity in sport: a qualitative exploration of sexual minority student-athletes in China

• Meng Xiang 1 , 2 ,
• Kim Geok Soh 2 ,
• Yingying Xu 3 ,
• Seyedali Ahrari 4 &
• Noor Syamilah Zakaria 5  

BMC Public Health volume  24 , Article number:  2304 ( 2024 ) Cite this article

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Metrics details

Sexual minority student-athletes (SMSAs) face discrimination and identity conflicts in intercollegiate sport, impacting their participation and mental health. This study explores the perceptions of Chinese SMSAs regarding their sexual minority identities, aiming to fill the current gap in research related to non-Western countries.

A qualitative methodology was adopted, utilising the Interpretive Phenomenological Analysis (IPA) approach with self-categorization theory as the theoretical framework. Participants were recruited through purposive and snowball sampling, and data were collected via semi-structured interviews, documents, and field notes. Sixteen former and current Chinese SMSAs participated in this study.

The study reveals four themes: hidden truths, prioritisation of athlete identity, self-stereotyping, and attempt. The results revealed that while SMSAs were common in intercollegiate sport, their identities were often concealed and not openly discussed. The predominant focus on athlete identity in sport overshadowed their sexual minority identities. Additionally, SMSAs developed self-stereotypes that influenced their thoughts and behaviours. The non-heterosexual team atmosphere in women’s teams led to the development of intimate relationships among teammates.

Conclusions

The findings from this study could be incorporated into existing sport policies to ensure the safe participation of SMSAs in Chinese intercollegiate sports. This research offers valuable insights for the development and implementation of inclusive policies. Future research in China could investigate the attitudes of coaches and heterosexual student-athletes toward sexual minority identities to inform targeted interventions.

Peer Review reports

Collegiate sport serves as a conduit for hope, competition, learning, success, and enhanced well-being for students [ 1 , 2 ]. Within this context, situated at the intersection of student-athlete and sexual minority identities [ 3 ], sexual minority student-athletes (SMSAs) experience more challenges than their heterosexual counterparts. Sexual minority constitutes a group of individuals whose sexual and affectual orientation, romantic attraction, or sexual characteristics differ from that of heterosexuals. Sexual minority persons are inclusive of lesbian, gay, bi+, and asexual-identified individuals [ 4 ].

In an effort to enhance the support of SMSAs in sport, Team DC, the association of sexual minorities sport club, awarded seven SMSAs the 2023 Team DC College Scholarship [ 5 ]. Besides the Team DC scholarship, there are the Rambler Scholarship, US Lacrosse SMSAs Inclusion Scholarship, NCAA Women’s Athletics Scholarship and Ryan O’Callaghan Foundation [ 6 , 7 , 8 ]. These scholarships were set up to make sport a more welcoming and safer environment for SMSAs. In particular, the Sexual Minority Scholarship echoes the International Olympic Committee’s framework of equity, inclusion, and non-discrimination, which states that everyone has the right to participate in sport without discrimination and in a manner that respects their health, safety and dignity [ 9 , 10 ].

Despite efforts by educational and sport organisations to foster inclusivity, research shows that the sport environment remains hostile to sexual minority individuals [ 11 , 12 ]. In intercollegiate sport, empirical evidence points to persistent negative attitudes [ 13 , 14 , 15 , 16 , 17 ], which are expressed through marginalisation, exclusion, use of homophobic language, discrimination, and harassment [ 17 , 18 , 19 , 20 ]. SMSAs frequently confront the difficult choice of disclosing their identity, often opting for concealment. Denison et al. found that SMSAs who disclose their identity to their teams may face increased discrimination [ 21 ]. Pariera et al. also observed deep-rooted fears among SMSAs of being marginalised by their teams upon revealing their sexual orientation [ 22 ]. Consequently, the hostile environment led to lower participation rates among sexual minority youth compared to their heterosexual counterparts [ 23 ].

In China, there is a lack of clear public policies related to the sexual minority population [ 24 ]. Despite homosexuality being removed from the Chinese Classification of Mental Disorders-3 in 2001 [ 25 ]. China’s stance towards sexual minority issues remains ambiguous. Many scholars describe this attitude as “no approval, no disapproval, and no promotion” [ 26 , 27 , 28 , 29 ]. Due to the lack of legal protection, sexual minorities frequently encounter discrimination. A Chinese national survey revealed that only 5.1% of sexual minority individuals felt comfortable being open about their gender and sexual identity in China [ 30 ]. This discrimination is particularly severe among Chinese sexual minority youth, who are at higher risk of bullying in school and college [ 31 , 32 ]. These youths face childhood victimisation [ 33 , 34 , 35 ], which heightens their risk of mental and behavioural health issues [ 36 , 37 , 38 ], including non-medical use of prescription drugs [ 39 ], depression [ 40 , 41 ], and suicide [ 42 ].

While sports participation is crucial for the well-being of sexual minority individuals, research on the sports participation of sexual minority youth in China is limited. The literature highlights a significant gap in understanding the status and circumstances of SMSAs in China. Most existing studies focus on Western populations [ 43 , 44 , 45 ], overlooking the unique sociocultural interactions affecting SMSAs in non-Western contexts, making it challenging for China to apply these findings. Furthermore, the lack of reliable research on the interactions between sexual minorities and institutions in Chinese higher education hampers a comprehensive understanding of SMSAs’ situations. This research gap impedes the development of effective interventions to foster inclusivity. Persistent discrimination and inadequate protective policies underscore the urgent need for academic, policy, and practical advancements to support sexual minorities in China [ 46 ]. Therefore, the aim of this study was to explore SMSAs’ perceptions of their sexual minority identity in Chinese sports, providing insights to guide the creation of supportive educational and organisational strategies.

Homonegativity and discrimination in sport

Homonegativity refers to any prejudicial attitude or discriminatory behaviour directed towards an individual because of their homosexual orientation [ 47 ]. Compared to the more common term “homophobia,” [ 48 ] “homonegativity” more accurately describes negative attitudes towards homosexuality [ 49 ] because the fear is not irrational but is learned from parents, peers, teachers, coaches, and the daily interaction environment [ 50 ]. Sport context is an integral part of society, and an extensive body of research has consistently demonstrated the presence of homonegativity in sport [ 12 , 21 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].

Homonegativity can manifest in forms such as verbal harassment, physical violence, or discriminatory behaviours. The “Out on the Fields” survey, conducted in 2015, represents the first large-scale international study focusing on homophobia in sports [ 60 ]. Participants were from six countries: Canada, Australia, Ireland, the United States, New Zealand, and the United Kingdom. It revealed extensive discrimination in sport, with a high percentage of gay men and lesbians experiencing verbal slander, bullying, threats, and physical assault. The OUTSPORT project, completed in 2019 and funded by the European Union, is the first comprehensive EU-wide study on homophobia and transphobia in sport. The project collected data from over 5500 sexual minority individuals across all 28 EU member countries [ 61 ]. The results revealed that a significant portion of participants faced adverse experiences in sport contexts related to their sexual orientation and gender identity, including verbal abuse, structural discrimination, physical boundary crossing, and violence. An overwhelming majority of respondents (92.9%) view homophobia and transphobia in sport as current issues. Additionally, 20% of respondents reported avoiding participation in sport due to concerns about their sexual orientation or gender identity, while 16% of active participants experienced at least one related negative incident in the past year. Notably, male student-athletes exhibited higher levels of homophobic attitudes compared to their female counterparts and non-physical education students [ 15 , 16 , 62 ]. Conversely, female athletes reported experiencing less fear of exclusion and a more inclusive team environment [ 22 , 63 , 64 ], highlighting significant gender disparities in homonegativity in sport.

Group and individual identity

The distinct team interaction inherent in sport may enhance or support expressions of homonegativity and discrimination, as Social Identity Theory posits that negative beliefs about certain groups may develop group identity [ 65 , 66 , 67 ]. This phenomenon is particularly noticeable in intercollegiate sport, where a strong emphasis on physical attributes and abilities often results in prejudices against those who deviate from established norms [ 16 ]. Such discrimination and mistreatment of SMSAs frequently stem from their teammates and coaches. Many SMSAs choose to conceal their sexual orientation due to fear of ostracism [ 60 ], with team members often identified as the primary perpetrators of discrimination [ 61 ].

Therefore, navigating sexual identity within intercollegiate sport is challenging for SMSAs, as their minority status becomes a focal point, impacting their overall experience [ 68 , 69 ]. They encounter a unique psychological and emotional burden, striving to reconcile societal norms and expectations with their true selves. This constant negotiation and management of their identity across different contexts further complicates their experiences, frequently leading to difficulties in maintaining authenticity [ 19 ]. Therefore, SMSAs in intercollegiate sport face intricate challenges in balancing their authentic identity with societal norms, significantly impacting their experience and sense of self.

Theoretical framework

Self-categorisation theory (SCT), an extension of Social Identity Theory, provides a valuable perspective for examining the perceptions of SMSAs in China, focusing on intragroup processes and individual navigation of personal and social identities [ 70 , 71 ]. Key principles of SCT, including self-categorisation, salience, depersonalisation, and individuality [ 67 ], are instrumental in understanding how SMSAs navigate their sexual identities within the confines of sport norms. Applying SCT, this study could explore the complex interplay of intragroup relations and identity processes among SMSAs in the Chinese sport context, underscoring how contextual factors distinctly shape their identity.

Purpose of the study

The purpose of this study is to explore SMSAs’ perceptions of their sexual minority identity within the Chinese sports context and understand how this identity influences their participation in sports. By illuminating the specific challenges and issues related to sexual minority identity in Chinese intercollegiate sports, this study provides a deeper understanding of the experiences of sexual minorities in this field.

Research design

This study was conducted with the interpretivist paradigm, which emphasises understanding the subjective experiences and meanings that individuals assign to their world. It posits that reality is not objective but is constructed through individual perceptions and social interactions [ 72 ]. Given the aim of exploring the perceptions of sexual minority identity in sport from SMSAs’ perspectives, a qualitative research approach is appropriate. In line with the purpose of the study, the Interpretative Phenomenological Analysis (IPA) was adopted in this study, an approach aimed at understanding people’s lived experiences and how they make sense of these experiences in the context of their personal and social worlds [ 73 ]. IPA research encompasses phenomenology, hermeneutics, and idiography and emphasises the personal significance of self-reflection among individuals with a shared experience in a specific context [ 74 ]. Additionally, IPA is particularly suitable for research focusing on identity and self-awareness [ 75 ]. The features and focus of IPA are consistent with the purpose of this study. Therefore, IPA was considered a suitable approach to explore the SMSAs’ perceptions of their sexual minority identity within the sport context in China.

Researcher characteristics and reflexivity

During the data collection phase of this study, the first researcher was a Ph.D. candidate and had obtained her Ph.D. by the time of this manuscript’s submission. Her doctoral committee continuously supervised the research. The first researcher’s doctoral committee members are proficient in qualitative research. The first researcher and the second coder have received systematic qualitative training, are skilled in qualitative analysis software (NVivo), and have published empirical studies using the IPA approach. Although none of the research team members were SMSAs, the first researcher and the second coder maintained long-term contact with SMSAs through their involvement in sport teams. The first researcher was a former student-athlete and is currently working as a coach. Given her background, she has had extensive time to interact with and understand SMSAs within student teams.

Participants and procedures

Purposive and snowball sampling methods were employed to recruit a homogeneous sample for this study, as recommended by Smith and Nizza [ 73 ]. Following approval from Universiti Putra Malaysia’s Human Research Review Committee, the researcher initially reached out to SMSAs within her network, subsequently expanding outreach through social media to reach a broader pool of potential participants. The participants were selected based on specific inclusion criteria (Table  1 ), ensuring relevance to the study’s focus. Of the 22 individuals contacted, 16 agreed to participate, while six individuals declined participation due to concerns regarding potential exposure. The sample included a diverse representation of sexual minority subgroups: one asexual man, four bisexual women, three gay men, and eight lesbians. Given the relatively low prevalence of asexual individuals [ 76 , 77 ], we only had one participant from this subgroup. Strict confidentiality measures were enforced, with participants assigned pseudonyms and their college affiliations omitted for anonymity. The demographic details of the participants are outlined in Table  2 .

In phenomenological research, the focus is on rich individual experiences rather than data saturation [ 78 ]. Similarly, IPA research aims to explore participants’ personal and social worlds through detailed, in-depth analysis [ 79 ]. Smith and Nizza [ 73 ] also highlighted that in IPA research, sample size is less crucial because of the emphasis on detailed analysis in small, homogeneous samples. Therefore, the richness of data and the depth of insight into each participant’s experience are more important than the number of participants or reaching data saturation. This study utilised IPA’s in-depth analytical approach with sixteen participants to provide detailed data. This methodological approach allows for a comprehensive exploration of individual experiences, aligning with the study’s objectives.

Data collection

Data for this study were collected through semi-structured interviews (Appendix A), allowing participants to choose the mode, time, and location, including face-to-face or online sessions on Chinese social networks. Each interview’s length is detailed in Table  2 , with an average duration of 63 min. Before each interview, participants signed informed consent forms following a detailed briefing on the study’s purpose and procedures. Given the sensitive nature of the research, the interviews were conducted solely between the researcher and the participant to ensure a safe and comfortable environment, fostering open and honest communication.

The methods of data collection exhibited some qualitative differences. In face-to-face interviews, participants were often cautious and hesitant to share personal experiences. Conversely, online interviews proved more effective, as participants felt more relaxed, leading to quicker rapport and greater openness. This difference likely stems from the reduced perceived risk of exposure in an online setting. Due to the clear objectives of the study and the structured interview guide, there were no differences between the data from current SMSAs and former SMSAs.

Notably, one participant provided data through written essays instead of a semi-structured interview due to concerns about exposure and discomfort. After discussing the matter, the participant agreed to respond to interview questions in written form. The first researcher sent the interview questions to the participant, who then provided written responses. Follow-up questions were asked based on these initial responses, resulting in four sets of essay responses. This approach, which aligns with the conventions of phenomenological research [ 80 ], allowed the participant to express their experiences comfortably. The essay data were analysed alongside the semi-structured interview data, with common themes identified across all responses.

Documents and field notes supplemented the data collection. Documents included photographs, videos, and diaries. With participant consent, these documents were analysed for relevance to the research purpose. Field notes captured contextual information during both face-to-face and online interviews, including keywords and participants’ pauses and intonations, with immediate elaboration post-interview to avoid biases [ 81 , 82 ]. These detailed notes contextualised data analysis [ 74 ] and contributed to the research’s credibility.

Data analysis

The data analysis in this study followed a seven-step process aligned with IPA research guidelines and contemporary IPA terminology. The data analysis procedure is depicted in Fig.  1 . The IPA analysis is iterative and inductive [ 83 ], involving the organisation of data into a structured format for easy tracking through various stages – from initial exploratory notes on transcripts to the development of empirical statements, theme clustering, and final group theme structure. The theoretical framework was incorporated at the final stage of empirical theme development.

To enhance the study’s validity, the first author invited another Ph.D. candidate to participate in the data analysis process. After the interview recordings were translated into transcripts using audio software, the first researcher listened to the recordings repeatedly to correct the transcripts. The second coder reviewed the recordings to ensure the transcriptions were accurate and verbatim. The first author employed NVivo software (released in March 2020) for coding, and the second coder utilised manual coding. All data were analysed in Chinese to maintain linguistic integrity and then translated into English for theme presentation.

Data Analysis Procedure. Adapted from Smith et al. ( 74 )

The procedures of this study adhered to the COREQ Checklist [ 84 ] (Appendix B) and the IPA Quality Evaluation Guide [ 85 ] to ensure rigour. The research met the good quality requirements for IPA studies as outlined by Smith [ 85 ] (Table  3 ). Throughout the research, emphasis was placed on internal validity, external validity, and reliability to maintain the study’s rigour and quality. The methods employed to address these aspects are summarised in Table  4 .

This study explored SMSAs’ perceptions of sexual minority identity within intercollegiate sport in China. From the perspective of SCT, the results uncovered four key themes from SMSA’s team-based interactive experiences. The research themes, along with their corresponding sub-themes and occurrences, are presented in Table  5 .

Hidden truths

The hidden truths refer to facts, scenarios, or knowledge that are not commonly known or readily available. In this study, the existence of SMSAs in intercollegiate sport was undeniable, yet it remained concealed due to the prevailing lack of transparency.

SMSAs are common in sport

This research uncovered the extensive existence of SMSAs in Chinese sport. Almost all participants acknowledged the ubiquity of sexual minorities in sport, with 12 out of the 16 participants specifically highlighting the presence of SMSAs in collegiate sport:

I think everyone is generally aware of sexual minorities; all people are aware of them to a greater or lesser extent. It is generally agreed that the existence of sexual minorities is a common phenomenon in modern society, and even more so in Sport, as anyone involved in sport knows that (Adam).

Participants frequently described the presence of SMSAs in intercollegiate sport, using terms like “widespread”, “common”, “normal”, and “quite many”. Several participants also provided specific details about the number of SMSAs in their respective teams. Jackie remarked, “At that time, half of my teammates were lesbians” (Jackie). Similarly, Zoe noted the significant presence of SMSAs in her team, “I think it (the number of SMSAs) was almost half of the team at that time. But I don’t know about the senior players; almost half of our junior players were SMSAs” (Zoe).

Silent identity

Participants noted the prevalence of SMSAs in sport but also emphasised the difficulty of openly discussing sexual minority identity in this context. They described the sport environment as reserved and lacking open conversations about SMSAs and their experiences.

The reticent nature of sport teams regarding sexual minority identity was evident in their attitudes. William observed, “I feel like most of my teammates just don’t take a stand. They don’t want to make a statement about SMSAs. Nor did they say they supported it or didn’t support it” (William). Similarly, Mia considered sexual minority identity as a personal issue, inappropriate for open discussion.

No one wants to ask or discuss this openly…we live in a very conservative environment all the time, and none of this content is something that teammates should be concerned about, and people would feel offended if you don’t handle it well (Mia).

Some SMSAs viewed avoiding discussions on sexual minorities in sport as respectful to teammates, aiming for a comfortable, stress-free environment. Joy said, “We came here to play, right? I don’t think any of the other players want to feel phased by who you are” (Joy). Mia echoed this sentiment:

…in team training, the game is the game, and I rarely bring other emotions into it…. In the company of most of our teammates, we don’t interact with each other in that way. It’s probably a default rule that respect is distance, I guess (Mia).

Charlotte, involved in volleyball and basketball, recounted a teammate’s public derogation due to her sexual minority identity, an incident not openly addressed by the team. She perceived sexual identity as a “taboo” topic. The narratives revealed a cautious approach among SMSAs towards expressing their sexual minority identity in sport. They felt compelled to carefully manage their sexual orientation, minimising its disclosure. This hesitancy likely stemmed from the existing reticence and limited acceptance of SMSAs in sport, fostering a sense of invisibility and concern over potential negative consequences.

Prioritisation of athlete identity

The theme of prioritisation of athlete identity suggests that for SMSAs, their identity as an athlete may play a more prominent or influential role in shaping their self-conception compared to their sexual minority identity.

Be an athlete

Several participants believed their primary role as student-athletes was to engage in sport, and they valued this aspect of their identity significantly. Joy expressed this sentiment, “I love volleyball very much … I don’t care much about relationships; I just love volleyball, and I think we are all here to do this, and nothing else matters. You don’t need to stress about it (sexual minority identity)” (Joy).

Emma echoed a similar perspective, noting, “I think my teammates are very professional; our program requires a high technical standard, and we spend most of our time training; other than that, things don’t seem that important” (Emma). When queried about the importance of sexual minority identity, she responded, “Yes, at least not concerning sport performance, or maybe it will have a bad effect” (Emma). Additionally, some participants felt that in the context of sport, sexual minority identity might be sidelined. Adam commented:

“We don’t share it (sexual minority identity) unless someone asks. We’re a team first, and then we’re individuals, and for me, I’m important personally, but in the team, we all probably need to sacrifice some of ourselves to make the team more united and stronger” (Adam).

Participants’ views as both student-athletes and sexual minorities highlighted contrasts in the intercollegiate sport environment. Their student-athlete identity was key in shaping self-perception and fostering a sense of community, while their sexual minority identity was often marginalised in aspects of interpersonal relations, team support, and self-identity development.

Sport performance first norms

In team sport, leaders are crucial in creating inclusive spaces for SMSAs and setting behavioural and attitudinal standards, including those towards SMSAs. In this study, some participants believed that coaches’ criteria for acceptance of sexual minority individuals or intra-team romantic relationships were based on athletic performance.

Some coaches firmly believe that team relationships negatively impact team performance and, therefore, strictly prohibit romantic relationships between teammates. Joy recalled,

She couldn’t accept that… she thinks being an athlete like that is ridiculous. It would make a mess; her team would be in a mess. She said you two are dating and that playing will affect your emotions, which means she meant to say there is no way I can treat another girl as a normal teammate… (Joy).

In contrast, some coaches adopt a more tolerant attitude. Jackie’s coach believes that if the team’s overall performance is not affected, issues such as sexual orientation or team relationships can be ignored. Jackie stated, “My coach is male and old, but he should know what’s going on, especially since our captain has dated several teammates and the coach pretends not to know. He would only care if we were winning games” (Jackie).

Whether it instructs prohibition or an indifferent attitude, both narratives reflect that the team’s norms for inclusivity are based on sport performance. These norms also influence how SMSAs assess their own sexual minority identity within the team, as Adam said:

As of now, I have someone in the team that I have a crush on and haven’t dated. Maybe if he and I argued over training or a game, it would affect the performance of the team and the relationship between teammates…. I don’t think I could let that happen (Adam).

The participants’ narratives emphasise how the “Sports Performance First” norms influence the attitudes and behaviours of coaches and SMSAs within the team. These norms not only shape the team culture but also profoundly affect how SMSAs navigate their identities and relationships in the team environment.

However, the excessive focus on sport performance highlights the athletic identity of student-athletes while neglecting their other identities, especially those of sexual minorities. This singular focus leads to the neglect of the personal needs and diverse identities of athletes. Although these measures may seem to ensure the overall performance of the team, they overlook the psychological health and holistic development needs of the individuals.

Self-stereotyping

Self-stereotyping denotes the tendency of SMSAs to describe themselves using stereotypical attributes in the sport context. These descriptions frequently align with stereotypical perceptions prevalent in the external environment. SMSAs tend to be perceived as having specific physical traits or behavioural tendencies.

Specific physical traits

Sophia provided an illustrative example of self-stereotyping through her personal experience. She commented:

In the beginning, I would think that if you are an SMSA, you must fit some characteristics. For example, at that time, I saw some lesbians in my team who had short hair or wore baggy t-shirts; I was a bit frustrated by my long hair and feminine appearance…and I felt that I might not quite fit those criteria. So, then I cut my hair and even wore a wrapping bra to the training ground (Sophia). Sophia’s narrative underscores how the pressure to conform to certain physical traits led her to change her appearance to fit the stereotypical image of an SMSA within the sport context.

Behavioural tendencies

In addition to physical traits, SMSAs also feel compelled to conform to certain behavioural tendencies that are stereotypically associated with SMSAs. Zoe explained, “Because of who I am (T), I felt I should have to perform stronger, so I put up with much training…. I felt I should be there to protect the other players; if I were vulnerable, I would look down on myself” (Zoe). This indicates a sense of obligation among some female SMSAs to embody strength, aligning with the stereotypical image of female SMSAs in sport. Conversely, male SMSAs in men’s teams often faced stereotypes of being fragile, weak, or exhibiting feminine traits. Royal noted that behaviours of some male SMSAs, like engaging in non-sport-related banter, led to gossip and negative perceptions within men’s sport. To avoid these stereotypes, Royal aimed to mimic the mannerisms of heterosexual athletes, as he explained:

I try to avoid being close to the team’s prominent male SMSAs and try to stay out of related conversations; I don’t want to be a standard gay; I want to have the same college life as the rest of the team (heterosexuality) (Royal).

Stereotypes in sport often forced SMSAs into roles incongruent with their authentic identities, significantly impacting their self-expression and identity. The pressure to conform to societal norms in sport settings created internal conflicts for SMSAs, challenging their ability to maintain their true sense of self.

This theme addresses situations where student-athletes engage in intra-team intimacy or mimic being SMSAs in sport. This attempt has two key elements: prolonged contact leading to intimacy and influence from sexual minority teammates.

Prolonged contact leading to intimacy

Participants noted that extensive training and competition schedules in sport fostered close bonds among team members. Lucas shared, “When we were preparing for the tournament, we trained together every morning and evening…the game spanned for almost a month, and after that, we felt as close as family to our teammates” (Lucas). Similarly, Ruby pointed out, “Back then, we were training every afternoon until late at night; it was quite hard (the training was very strenuous) … it lasted for six months” (Ruby). These prolonged interactions sometimes led to the development of more profound attractions among student-athletes.

“I think we had many moments of trust and intimacy together on the field that built up some heartfelt feelings. These feelings made me feel emotions beyond that of a teammate…. Then I realised that gender might not be so important because it’s hard to build that kind of relationship in a typical romance” (Savannah).

Influence from sexual minority teammates

Participants also described how interactions with sexual minority teammates led them to explore their own sexual identities, as illustrated by Ava’s recounting of her initial same-gender relationship experience:

That time we went out to a tournament, and I found that four of my teammates, three of them were lesbians…we didn’t have games at night, so they had been talking to their girlfriends every night on the phone, and I just felt as if that was not too bad. Probably influenced by them, I got a girlfriend at that tournament as well…. Even though we broke up when we returned, I could accept girls (Ava).

Mia described a similar experience:

There were some lesbians in my team, and then it just seemed natural that I got close to one of them…. Well, I was thinking about whether that relationship would affect the team. But then I found out that there were other couples on the team. So, I feel like I wasn’t doing anything wrong (Mia).

The phenomenon highlights the significant role of peer influence in team settings. When individuals are around many teammates in same-gender relationships, it fosters an environment that normalises such relationships. Notably, this influence is not coercive but stems from observing and interacting with teammates who are comfortable with their sexual orientations. This environment helps individuals feel accepted and more confident in exploring their identities and relationships.

This study explored the perceptions of SMSAs regarding their sexual identity within intercollegiate sport in China. Its importance lies in its contribution to understanding the complex realities of SMSAs in China, an area that has lacked depth in the literature. By reaffirming the necessity of examining these athletes’ experiences, this study reveals the intricate conflict between adhering to team norms and expressing personal characteristics within the context of the Chinese social and cultural background.

The results show that SMSAs are a recognised reality in Chinese intercollegiate sport, consistent with findings from Western countries. While precise figures of sexual minorities in sport may vary across countries, it is acknowledged that they are present at all competitive levels, from school and college sport to the professional sphere [ 22 , 86 , 87 , 88 , 89 , 90 , 91 ]. Although no national census on sexual minorities in China or in sports environments exists, related research indicates that many college and university students self-identify as sexual minorities. For instance, an online survey conducted across 26 colleges and universities in 10 Chinese provinces found that over 8% of students identify as sexual minorities [ 36 ]. Additionally, another national survey revealed that nearly a quarter of college students identify as non-heterosexual [ 92 ]. Recognising and addressing the unique challenges faced by sexual minority youth, who make up a notable percentage of the student population, is essential for sport and educational institutions.

Despite the apparent prevalence of SMSAs, the study confirms that their identities often remain hidden in the context of Chinese intercollegiate sport. This can be attributed to two main reasons: First is the concern about discrimination if exposed. Chinese sexual minorities frequently report experiencing abuse or discrimination in families, schools, and workplaces [ 93 ]. Additionally, conversion therapies and discriminatory counselling practices persist in mental health services [ 94 ], creating an environment where discrimination is a significant concern, thereby reducing the likelihood of SMSAs coming out in the sports environment. The second reason is the constraint of traditional Chinese culture. The dominant Confucian culture in China emphasises harmony, internalised homonegativity, and conformity [ 95 , 96 ], often at the expense of individual expression and identity development. This cultural backdrop influences how sexual minorities perceive their own identities [ 97 ] and creates an ideological constraint that leads to social rejection and resistance towards sexual minorities [ 98 ], thereby reducing the visibility of sexual orientation-related topics in the Chinese sport context.

Moreover, SMSAs in China often prioritise their athlete identity over their sexual minority identity, influenced by the attitudes of team leaders. This tendency is reinforced by coaches who primarily focus on the biological sex of athletes and lack training or understanding related to sexual minority issues [ 99 ]. Consequently, the Chinese coaches’ lack of knowledge about sex and sexual orientation exacerbates the silence surrounding sexual minority identities in the Chinese collegiate sport environment and intensifies the identity conflict for SMSAs. Emphasising athletic performance is central in sport but should not overshadow the holistic development of student-athletes. McCavanagh and Cadaret [ 100 ] noted that student-athletes might face challenges in reconciling various aspects of their identity in a heteronormative sport context. The suppression of sexual minority identity can lead to isolation from potential support systems that nurture positive sexual and gender identities. Prioritising athletic success over broader student development in sport departments limits growth opportunities for all students, including SMSAs. Chavez et al. [ 101 ] emphasised that student development requires recognising and valuing diversity, suggesting that a singular focus on athletic prowess can diminish the benefits of diversity among student-athletes. Embracing diversity is not only a personal journey but also one that can enhance the collective experience within sport settings.

In addition, self-stereotyping within SCT involves aligning one’s self-concept with the characteristics of valued social categories [ 102 ]. Latrofa [ 103 ] suggests that members of low-status groups, like SMSAs in sport, may self-stereotype to align more closely with their group, reflecting recognition of lower status and self-perception through peers. This study revealed SMSAs shape their self-identity based on the attitudes prevalent in their sport environment, with influences from peers and coaches being internalised as personal attitudes [ 104 ]. Such self-stereotyping supports maintaining a favourable social identity and adhering to group norms but can reinforce negative stereotypes and prejudices within sport.

Internalising stereotypes may lead SMSAs to develop prejudices against themselves and others, perpetuating discrimination. It can also hinder individual development, impacting self-esteem and confidence. For example, aligning with negative stereotypes could cause SMSAs to doubt their worth and capabilities, affecting emotional well-being and satisfaction. Liu and Song’s [ 105 ] survey of Chinese college students illustrated the direct impact of gender self-stereotypes on life satisfaction, highlighting the significant effects of self-stereotyping on individual well-being.

Furthermore, in the context of traditional and reserved Chinese culture, intercollegiate sport offers a relatively free and open space for sexual minority women. The results of this study suggest that the visibility of sexual minority women in teams and the long time spent together allow these athletes to explore and establish intimate relationships. These results are similar to findings in Spanish studies [ 103 ], which highlighted the protective and liberating role of sports teams in the sexual exploration of female sexual minority athletes. Research by Organista and Kossakowski on Polish female footballers [ 106 ] and Xiong and Guo [ 96 ] on Chinese women’s basketball teams also revealed a climate of non-heteronormativity in women’s sport. These climates provide a sanctuary from heterosexual pressures, allowing sexual minority athletes to engage in sport free from traditional constraints. Such environments help female sexual minority athletes navigate and subvert heteronormative norms by cultivating supportive subcultural networks within their teams.

This study addresses the lack of in-depth research on the experiences of SMSAs in Chinese intercollegiate sport. It fills the gap by exploring the complex realities of SMSAs, focusing on their identity conflicts and the influence of the Chinese social and cultural background. Specifically, this study provides valuable insights that align with SCT [ 71 ]. This study addresses a notable gap in the existing literature regarding sexual minority sport participation, as rarely have these perceptions been explored. Drawing from the lens of SCT, the results of this study revealed several valuable insights into how their sexual minority identity impacts their participation in sport. These findings not only enhance our understanding of how SCT applies to the sport experiences of sexual minority individuals but also contribute to the advancement of SCT in research on sexual minority sport participation. The themes uncovered in this study closely align with central SCT concepts such as identity salience, self-stereotyping, and depersonalisation, illuminating the ways SMSAs comprehend and express their sexual minority identity within the intercollegiate sport context. SCT, with its focus on both intragroup and intergroup relations within the multifaceted construct of the self, offers valuable insights into the complexities of SMSAs’ self-perceptions and the intricacies involved in developing and manifesting their identities in the realm of sport.

Based on the results, more effort needs to be put into understanding sexual minority identities in intercollegiate sport. By examining the perspectives and experiences of SMSAs, we can gain insights into the interactions and influences of sexual minority individuals in the sport context. The interplay between an individual’s self-perception and situational dynamics results in a self-identity that mirrors the collective. In addition, the prevalent pressures and normative prejudices inherent in the sport system significantly influence their self-identity. Therefore, valuing SMSAs’ understanding of their self-identity shows respect for each person’s differences and rights. We hope the findings will be incorporated into existing sport policies to promote inclusivity and ensure safe participation for sexual minority students. To encourage and support the full development of SMSAs, college athletics and related institutions should prioritise understanding and respecting their perceptions of their sexual minority identity. By doing so, institutions can create a more inclusive and supportive environment that acknowledges and addresses the unique challenges faced by SMSAs.

Nevertheless, caution should be exercised when generalizing the findings, especially for subgroups with low representation, such as asexual individuals. While the study provides valuable insights into SMSAs’ perceptions of their sexual minority identity within the Chinese sport context, the limited number of asexual participants means their unique perspectives may not be fully captured. Therefore, these findings may not fully represent all sexual minority subgroups.

Future research could focus on exploring the perceptions and experiences among various sexual minority subgroups within sport participation in China. Additionally, considering the cultural diversity across China’s vast geographic regions, it would be valuable to examine how SMSAs perceive their minority identity in different cultural contexts. Given the scarcity of related studies in China, it is also important to survey other stakeholders in the sport environment, such as coaches and heterosexual student-athletes, to gain a broader understanding of perceptions of sexual minority identities. These insights can inform the development of targeted interventions aimed at ensuring the safe and inclusive participation of SMSAs in intercollegiate sport.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to ethical considerations but are available from the corresponding author on reasonable request.

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Conceptualisation, MX.; methodology, MX; data collection, MX and YX.; data analysis, MX and YX; data curation, MX; writing—original draft preparation, MX; writing—review and editing, KS, SA, and NZ; supervision, KS, SA, and NZ. All authors have read and agreed to the published version of the manuscript.

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Xiang, M., Soh, K.G., Xu, Y. et al. Navigating sexual minority identity in sport: a qualitative exploration of sexual minority student-athletes in China. BMC Public Health 24 , 2304 (2024). https://doi.org/10.1186/s12889-024-19824-9

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Explaining the burden of cultural factors on MS disease: a qualitative study of the experiences of women with multiple sclerosis

• Fahimeh Pourhaji 1 , 5 ,
• Mousa Mahdizadeh Taraghdar 2 ,
• Nooshin Peyman 1 , 4 ,
• Jamshid Jamali 3 , 4 &
• Hadi Tehrani 1 , 4  

BMC Women's Health volume  24 , Article number:  477 ( 2024 ) Cite this article

Metrics details

Multiple sclerosis (MS) is a debilitating, non-traumatic disease that is common among young adults. Cultural factors, as background factors, can affect how patients adapt and their quality of life. This study aimed to explain the burden of cultural factors on Multiple sclerosis.

This study was conducted with a qualitative approach and conventional content analysis among women with Multiple sclerosis in Mashhad. The data were collected through semi-structured interviews with women with MS. Fifteen patients with Multiple sclerosis were selected using purposeful sampling. The Graneheim and Lundman method was used to analyze the collected data. The transferability of the study was evaluated using the Guba and Lincoln criteria. MAXQADA 10 software was used to manage and analyze the data.

In explanation of the cultural factors of patients with Multiple sclerosis, one category (cultural tensions) and five subcategories (forced communication with spouse’s family, definition of women’s role in society, people’s behavior, social beliefs and isolation of the patient) were extracted.

The results obtained in this study show that female MS patients face various concerns. Overcoming these challenges require a change in the attitude of people in the society towards women with MS, which is important in the context of formulating practical policies to create a suitable culture. Adopted policies should aim to internalize the culture of changing society’s views of female MS patients. Therefore, the authors argue that there is a need for cultural policies, followed by the systems implementing these policies to consider the challenges mentioned in this study as a priority for MS patients.

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Multiple sclerosis (MS) is a chronic inflammatory autoimmune disease of the central nervous system (CNS) [ 1 ]. Multiple sclerosis is usually diagnosed between the ages of 20 and 40 [ 2 ]. The prevalence and incidence of MS is expanding worldwide, and the prevalence of MS is estimated to be 35.9 persons per 100,000 population (2.8 million people) in 2020 [ 3 ]. The probability of MS in women is almost to 2–3 times higher than that in men [ 4 , 5 ]. The prevalence of MS in women and men was estimated to be 44.8/ 100,000 and 16.5/ 100,000, respectively [ 6 ].

People suffering from this disease need to deal with and adapt to its everlasting challenges [ 7 ]. Cultural practices and beliefs of patients affect their coping style with challenges. Due to the abundance and complexity of a person’s relationship with the society and its culture, the influence of cultural factors on human behavior cannot be ignored [ 8 ].

Every culture has certain views, behaviors and beliefs that not only affect people’s way of life, roles and their worldviews, but also affect the health and the numerous illnesses that plagues the people in the society [ 9 ]. Cultural differences can influence the concepts of health and diseases of societies and each society creates ways to treat and manage illnesses based on its culture [ 10 ].

The word “culture” refers to systems of knowledge, concepts, rules and activities that are learnable and are passed down from one generation to another [ 11 ].

Culture influences the ways of describing and understanding patients’ experiences and visible behavioral cues in clinical encounters, symptomatology, clinical manifestations, treatment expectations, adaptation to the disease, and treatment responses [ 12 ].

Psychosocial stressors and cultural characteristics may cause problems in communication and affect the diagnosis and treatment [ 13 ]. Sociocultural norms and beliefs lead people in society to have certain expectations based on specific gender roles for women [ 14 ].

For example, the irrational expectations of the spouse’s family and their great influence on the spouse’s decisions has become the basis for more worries in life [ 15 ]. Studies show that the main notions of the way of doing the housework have not changed much for men and women. They also show that housework and taking care of children are still considered the main duties of women [ 16 ].

On the other hand, the unequal distribution of power, resources and responsibilities between men and women has been institutionalized traditionally and has even taken root in women’s own thoughts and has become a rule. In many societies, women are in charge of nutrition, immunization, cleaning, hygiene, management, organizing ceremonies and celebrations without receiving wages, and women must perform tasks such as cleaning, raising children, cooking, and taking care of their husband [ 17 ]. Having children is considered one of the main goals of marriage [ 18 ]. Therefore, social and cultural norms and beliefs impose significant pressure on women by imposing gender expectations on them [ 14 ].

Considering that MS is more common in women of reproductive age, it may have a significant and long-term effect on women and families who are affected by this condition [ 19 ]. One view about pregnancy and the birth of a baby from a mother with MS is that pregnancy is potentially dangerous for the mother and her baby [ 19 ].

Another common concern in MS patients is sexual dysfunction. Sexual dysfunction is a very common and devastating problem in people with MS [ 20 ]. Young women with MS face challenges in finding a partner, raising a family, and managing their sex lives [ 21 ]. Studies show that patients are very discouraged by the behavior of people around them and their seemingly sympathetic advice [ 22 ]. A major issue in MS employment research is the strong effect of disease status on occupational participation [ 23 ]. Studies show that MS affects the occupational status of patients in various ways. Discrimination in the workplace may result in wrongful termination or failure to provide reasonable accommodations [ 24 , 25 , 26 ].

A qualitative study conducted explained the psychosocial factors of the burden of illness and demonstrated that patients with multiple sclerosis experience stress, agitation, and stigmatization [ 27 ]. However, in addition to the psychosocial challenges, women with MS also experience other challenges. Some of these challenges can be related to beliefs, perceptions, and cultural barriers [ 28 ]. In the long term, insensitivity to cultural aspects can have adverse effects on health and thus perpetuates health inequalities [ 29 ].

Many studies have addressed one or two cultural factors affecting the occurrence or exacerbation of MS; however, due to the diversity and extent of cultural factors, none of them provided integrated classification of these factors and the true contribution of cultural factors on the health outcomes of MS patients is not understood. Since studies have not specifically focused on cultural factors affecting MS, in this study, an attempt was made to understand and classify cultural factors that are effective in exacerbating MS. Cultural beliefs are crucial for providing adequate care and support, and efforts to break cultural barriers also enable better care for people with MS. By addressing this research gap, we can help to develop effective cultural interventions. Which ultimately leads to improving the attitude and reducing the health disparity of MS patients.

This study employed a qualitative approach with conventional content analysis to examine women patients with multiple sclerosis (MS) in Mashhad, Iran, a major city located in the eastern part of the country, during the year 2022.  

Participants and recruitment

In this research, the purposeful sampling method was used and the sampling continued until data saturation was reached.

Considering the maximum diversity in social situations in the city level (from different geographical, urban, and rural areas and different ages), the participants were selected from among 4600 patients admitted to the comprehensive MS center and the MS association. Age, duration of illness, level of education, marital status, and occupation were among the important underlying factors considered in this study.

With official permission, the researcher went to the Comprehensive MS Center and the MS association. She explained the purpose and importance of the research to the officials, and the necessary coordination was conducted with the officials of each department. Then, while communicating with the patient, the researcher explained the objectives, the importance of the research and the conditions of the research to the patients. Participants were allowed to bring their family or caregiver with them during the interview. However, all participants preferred to be interviewed alone. Finally, the patients who wanted to participate in the interview were selected and were interviewed after the necessary coordination.

Inclusion and exclusion criteria

Iranian patients with MS (according to the 2017 McDonald criteria) [ 30 ] and residents of Mashhad, can participate in this study after voluntarily completing and signing the consent form, if at least one year has passed since their MS was diagnosed by a physician. The exclusion criterion was the unwillingness of the participants to continue their cooperation at each stage of the research.

Ethical considerations

Before the interviews, the objectives and importance of the research were explained to the participants. The patients were assured that all information and interviews are only for research purposes and confidentiality and anonymization of information is respected in all stages. The participants’ voices were recorded with their permission. All the stages of this study were conducted following the Helsinki Declaration.

Data collection and analysis

Face-to-face semi-structured interviews were conducted with MS patients to collect data. After twelve patients were interviewed, the data became saturated because no new concepts were obtained from the interviews, and the codes and concepts were repeated. It should be noted that three more interviews were conducted to ensure data saturation. Interviews were conducted by the first author (FP: who is a PhD student in health education and health promotion). The interviewer had experience and interest in conducting interviews on factors related to MS. No of the participants withdrew from the study. Two test interviews were conducted to assess the validity of the tool.

Quantitative and qualitative articles on the cultural factors affecting MS worldwide were reviewed to guide the related questions. The questions were then formulated and revised according to the experiences of the research team. The guide questions are provided in Supplementary File 1 .

For example, in this study, questions were asked such as has this disease affected your relationships with others? If yes, how? Can MS affect others relationship with your? Explain it. What factors cause you worry? Explain it.

At the end of any interview, the researcher asked the participants to talk about any topic they wanted, which the researcher did not mention. The interviews were conducted in the MS Comprehensive Center that was suitable in terms of ventilation, light, and sound.

The interviews were conducted between July and September 2022. Depending on the participant’s tolerance level and environmental factors, the duration of each interview varied from 30 to 63 min. In order for personal thoughts to not affect the process of data collection and analysis, the researcher wrote down her thoughts on paper to avoid emphasizing them.

During the study, the researcher carefully observed the participants’ behaviors in terms of feelings, emotions, and reactions. The researcher then added notes collected during the observations to the interview margins. The researcher provided her contact information to the participants so that wherever the participants felt the need to provide more detailed information to the researcher, they could contact the researcher.

A qualitative data analysis was performed using the five-step approach proposed by Graneheim and Lundman. In the first stage, immediately after completing the interviews, the recorded interviews were written on paper to create the primary data. In the second step, the texts were read several times to obtain a general understanding of their content. In the third step, the textual content is divided to determine semantic units and basic codes. In the fourth step, to obtain more comprehensive categories, the primary codes were classified based on their similarities and differences. In the fifth stage, the main subject of each category was determined [ 31 ]. First, the voices of participants were voluntarily recorded using a mobile phone recorder. Data backup was ensured by using two voice recorders. After each interview, the recorded interviews were transcribed on paper and were read several times. In the next step, handwritten transcripts were typed in Word 2016 as the primary research data. Typed interviews were analyzed using MAXQDA version 10 software. In the next step, the important sentences and phrases were first determined, and then the words and sentences (semantic units) were coded by the researcher, and open codes were formed. After the initial codes were extracted, those that were semantically and conceptually similar or related were classified into a category and formed a subcategory of a single topic with a higher level of abstraction. After the formation of more comprehensive categories, the analysis process continued to create the main and subcategories. To check the created codes, an independent researcher also checked the codes, and if there was an unresolved difference between the first and second researchers, the third researcher entered to resolve the difference (Fig.  1 ).

Methodological considerations

Validity, verifiability, transferability, and reliability are the four standards of Lincoln and Guba that were considered to strengthen the data [ 32 , 33 ]. To validate the data, interviews were conducted with various patients. The extracted codes and texts were shared with several interviewees. To review the transcribed interviews, several meetings were held with the team leader.

To ensure the correctness of data, some of the codes and free subsets were checked by experts. In addition, part of the interpretation of the participants was checked by the participants themselves and corrections were made wherever needed.

To assure the transferability of the data, a purposeful sampling method was used, and sampling was performed with maximum diversity and continued until data saturation. The researchers increased the transferability of the data by providing a detailed and step-by-step description of how to conduct the research process, and also described the characteristics of the studied population in order for other researchers to follow the research process. To check the reliability, the research team revised the process in two steps. In the first stage, partial reliability control was carried out and that the researchers checked the categories and coding instructions after working with 10–50% of the data. In the second step, the general reliability was examined by listing the final category at the end of the task (Fig.  1 ).

Flowchart of methodological steps

Tis study was performed through semi-structured interviews with 15 patients with MS. Participants ranged in age from 27 to 52 years with a mean age of 37.13 years old and standard deviation (± 7.49). Participant’s duration of the disease ranged in from 3 to 22 years with a mean of 9.99 and standard deviation (± 6.46). (Table  1 ).

Analysis of the 15 interviews resulted in 138 extracted codes, and after exclusion of the duplicate codes, a total of 22 main codes remained. These codes were classified into five sub-categories (Obligatory communication with the spouse’s family, definition of women’s role in society, people’s behavior, societal beliefs and patient isolation) and one main categories (cultural tensions). More complete results can be seen in Table  2 .

In the following, we will describe each of the categories.

Main category: cultural tensions

First subcategory: obligatory communication with the spouse’s family.

The participants reported that obligatory communication with the spouse’s family such as unwanted participation in parties, tolerating snide comments and remarks for the sake of their spouse, keeping the spouse’s family happy, recurring arguments, jealousy, turning their husband against them, and constant judgments were associated with worsening symptoms.

Participant No. 7 (Duration of illness 3 years): “… my husband doesn’t know about my illness, I don’t want him to know about my illness, because I’m afraid that my husband will tell his family, I haven’t visited my husband’s family for a long time, because communicating with them bothers me. Fortunately, now that I don’t see them anymore I feel much better. That’s why I’m calm. That’s the only reason. When I look back, I ask myself why did I make such a mistake! I ask myself, when they create so many problems for me and my children, why did I go and see them in the first place? Why did I keep telling myself that no, you should go visit them for the sake of your husband?”

Participant No. 8 (Duration of illness 13 years): “…there are many people who get on my nerves, like in the beginning my mother-in-law used to tease me a lot. It’s because of my mother-in-law’s behavior that I’m not well and that I’ve reached this point. Whenever I went to their house I used to come back sad and crying. Now it’s been forty days since the last time I went to their house. The last time, she said something to me again that made me very upset, my husband wanted us to go to his mother’s house but I said I won’t come to their house anymore, you go. There are some people whom you cannot break your relationship with. My mother-in-law disregards my illness, even though she knows that I am sick, she still speaks her mind.”

Second subcategory: definition of women’s role in society

In this context, the participants consider societal beliefs about women’s roles such as taking care of their husband and children, housekeeping, being an income generator, keeping up appearances, meeting the expectations of the family, and having adequate sexual activities to be effective.

Participant No. 11 (Duration of illness 5 years): “… I have four children; I don’t have time to exercise at all. All day long, I am doing housework, taking care of children and cooking. I told my husband: One of the patients is participating in training classes, exercise classes, flower-making classes and goes out of the house sometimes and her condition has improved a lot, but he said to me: If I were that woman’s husband, I would definitely divorce her.” my husband believes that the wife’s job is doing housework.”

Participant No. 3 (Duration of illness 3 years): “… I tried very hard to always have good and perfect marital life, because my husband cares a lot about sexual matters. Sometimes I am not physically and mentally ready to have sex, but I know that I am obligated to have sex with my husband. Because if I say that I don’t want to have sex, my husband says: It’s because of your MS that you have a lower sex drive.”

Third subcategory: people’s behavior

In this context, the participants consider people’s behavior such as pitying the patient, changes in people’s views, and backbiting the patient’s family members to be effective.

Participant No. 4 (Duration of illness 9 years): “…we are like other people, we don’t like being pitied by others and people changing their behavior because of our illness, we are just like them, we are people, we have a life, only our condition has become a little more difficult, that’s all.”

Participant No. 7 (Duration of illness 12 years): “… I don’t have any expectations from the people around me, people are bad, if they find out that you have a problem, they take advantage of you.

Fourth subcategory: societal beliefs

In this context, the participants consider societal beliefs such as the belief that the patient should not get married nor get pregnant, the patient being a burden, and not having a place in society to be effective factors.

Participant No. 10 (Duration of illness 3 years): “…for now, my husband said: go and take the appropriate medicine and treatment, when your body reaches a stable state, then we will have a baby. It is not advisable to have a baby now. Of course, I’m also afraid, everyone also says it won’t be too late to have a baby, you should wait for a while.”

Participant No. 2 (Duration of illness 13 years): “… but one of the main reasons for choosing Cinnovex was that it was free, because I did not want my husband to pay for these issues. My husband does not say that he won’t pay, but I would be very upset if I became a burden on my husband.”

Participant No. 14 (Duration of illness 5 years): “… Everyone thinks of ideal things when getting married, and everyone wants to choose a beautiful, healthy, and rich girl for their son. I am very worried. If you say you are sick before marriage, they refuse to marry you.”

Fifth subcategory: patient isolation

In this context, participants consider behaviors that lead to patient isolation such as reduced participation, ignoring the patient, and lack of trust in the patient, to be effective factors.

Participant No. 2 (Duration of illness 13 years): “…I didn’t tell the company that I was sick. If a private company knows that you are sick, they won’t hire you. Private companies are so conservative because of insurance issues, and most of the people there do not have a positive attitude and they aren’t fun people to work with, everyone is looking for their own interests and they might even take advantage of your illness, for example, the moment something bad happens, they will say that this lady has a problem and she is not fit for working in this company.”

Participant No. 7 (Duration of illness 12 years): “… I am his big sister, but without telling me anything, they went to propose for my brother, and they preferred not to include me. Then they said: We were afraid that something would be said during the proposal that would upset you. We didn’t tell you anything, for your own sake. But it was pretty clear that they didn’t want me, who is sick and stutters because of my MS, to go to the proposal with them. They thought to themselves that if the girl’s family saw my condition, they wouldn’t let their daughter marry my brother.”

Participant No. 5 (Duration of illness 13 years): “… I was very stressed at work and my boss was very pushy about whether the work was done correctly or not. It was as if he did not trust my performance. Last time there was a fierce fight between us which made me leave the company, and again in the next company, there were still many challenges that were bothering me.”

This study aimed to explain the burden of cultural factors on Multiple sclerosis. Based on the results, one main category (cultural tensions) and five subcategories (obligatory communication with the spouse’s family, definition of women’s role in society, people’s behavior, societal beliefs and patient isolation) were constructed.

The results of this study showed that female MS patients are often forced to communicate with their spouse’s family in order to keep their spouse satisfied and prevent marital disputes. This obligatory communication may impose a lot of psychological pressure on them, which can lead to the exacerbation of the disease.

The behaviors of the spouse’s family, such as their interferences, jealousy, selfishness, gossips, vilification, insults, disrespect and their objections to the lack of sociability of the husband and wife cause consequences such as increasing worries, increasing tension between spouses, and the emergence and onset of depression symptoms [ 34 , 35 ]. The results of Datta’s study showed that conflict with the husband’s family and especially the mother-in-law is a fundamental issue in the topic of marital conflicts [ 36 ]. A dissatisfying relationship with one’s mother-in-law is an important risk factor for married women, which endangers their health [ 37 ].

The results of this study showed that female MS patients, due to the societal expectations of women’s role, are often forcing themselves to act a certain way in order to keep their husbands happy and avoid marital disputes. This forced relationship may impose a lot of psychological pressure on them, which leads to the exacerbation of their disease.

The main views of men and women about how to do housework have not changed much, and housework and taking care of children are still considered the main duties of women [ 38 ].

The role of women has changed due to economic conditions and social demands, women have to endure tremendous pressure to get a job similar to their men counterparts, while having to maintain an active role in their personal life [ 39 ].

Work-life balance is a key issue in all types of jobs due to dual-career families becoming more common and stressful jobs with long hours becoming the norm. Work life integrated with personal life creates stress [ 40 ].

People with MS, considering the types of sexual dysfunction and its indirect effects on mental health, quality of life and intimate relationships, may see sexual dysfunction as the most negative feature of this disease [ 41 ].

The results of this study showed that female patients with MS often experience changes in the views and behaviors of those around them, and these behavioral changes are disturbing and lead to the exacerbation of their disease.

A study showed that being pitied by others is an uncomfortable situation that is characterized by a lack of understanding of the situation [ 42 ]. Another study showed that most patients notice a change in other people’s opinions of them after the diagnosis [ 43 ].

The results of a study showed that the patients’ families believed that because of the label of the disease, in addition to the patient themselves, the families are also treated differently. They felt that they were judged negatively and were simply ignored [ 44 ].

The results of a study showed that the support of the surrounding people should be such that it does not cause the patients to be dependent on them or create a feeling of being pitied in the patients, so that they can find the identity and purpose of their lives in post-illness conditions [ 45 ].

According to our study, the exposure of female MS patients to societal beliefs increases negative feelings, such as avoiding marriage and feeling like a burden, and these negative feelings are the basis for the exacerbation of the disease.

Women would avoid having children due to the false belief that it would worsen the overall course of the disease [ 46 ].

MS is diagnosed in adulthood and is more common in women. Therefore, many women with this disease are discouraged from starting a family when their disease is diagnosed [ 47 ].

Although pregnancy has been shown to have no effect on MS and MS to have no effect on pregnancy, some women may still be discouraged by some family members and health professionals [ 19 ].

Some patients expressed their discomfort with feeling like a burden and that their family is wasting a lot of time and money on them [ 48 ].

The importance of raising the awareness of family and community members about their possible negative influence on the MS patients and encouraging them to review their behaviors to prevent putting more pressure on the patients, should be emphasized [ 49 ].

Not considering a specific position for these people in society creates difficult and unfortunate conditions for them, especially those who had strong personalities and were influential members of society before contracting the disease [ 50 ].

Patients with physical disabilities believe that their functional limitations cause problems for their caregivers and significant others. Feeling like a burden may lead to distress and complicate the relationship with the caregiver [ 51 , 52 ].

Considering that many women with MS are vulnerable to societal beliefs, it is necessary to formulate policies to change these beliefs so that society can take a step towards positive changes, and to reduce the frequency of these behaviors.

The results of this study showed that female MS patients are ignored by others during the disease, and the trust of others in their abilities decreases. This Distrust may impose a lot of psychological pressure on them, which leads to the exacerbation of their disease.

The results of studies show that many MS patients face a challenging work life. A higher proportion of people with MS report unemployment, part-time employment or reduced working hours, and lower income compared to the general population [ 23 , 53 ].

MS is associated with work difficulties, reduced working hours or their involvement and participation in their workplace, being transferred to jobs or other departments that is below their skill or knowledge level due to their employers’ impression that they are unable to handle the stress or the pressure of such works, and termination of voluntary and involuntary work or unemployment [ 54 ]. Greiton et al. found that the gender of women with MS was associated with their rate of unemployment [ 55 ].

Negative encounters such as discrimination and uncertainty from colleagues, managers or supervisors, and work organizations contribute to job transfer and termination, while positive support is associated with organizational embeddedness and job continuity [ 56 , 57 ].

Women with disabilities have also historically faced double discrimination due to their physical disability and gender, and have been ignored in many parts of society [ 58 ]. Simmons et al.‘s study showed that many MS sufferers have difficulty keeping jobs, even in good economic times [ 59 ].

Most of the participants were dissatisfied with the normalization of the disease for the doctors, followed by their superficial response to the patient and the lack of sufficient attention to the patient, and this issue had reduced their motivation to pursue treatment and follow medical recommendations [ 60 , 61 ].

Research limitations

The results of this study are limited to explaining the burden of cultural factors on disease worsening in women with MS in the Iranian culture. Therefore, to benefit from the findings of this study, it is necessary to conduct similar investigations in other fields and cultures. Although the researcher tried his best to be neutral during the interviews. However, this important principle may not have been inadvertently observed. Finally, more research is needed to provide complete insight into the cultural factors associated with MS.

The results obtained in this study show that female MS patients face concerns. Overcoming these challenges require a change in the attitude of people in the society towards women with MS, which is important in the context of formulating practical policies to create a suitable culture. Adopted policies should aim to internalize the culture of changing society’s views of female MS patients.

Therefore, the authors argue that there is a need for cultural policies, followed by the systems implementing these policies to consider the challenges mentioned in this study as a priority for MS patients.

Data availability

The data sets used and/or analyzed during the current study was available from the corresponding author on reasonable request.

Abbreviations

• Multiple sclerosis

Central Nervous System

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Acknowledgements

This article is a part of the Ph.D. thesis in the field of Health Education and Health Promotion sponsored by Mashhad University of Medical Science and research project approved by Ethics Committee of Mashhad University of Medical Sciences with the code of ethics IR.MUMS. FHMPM.REC.1400.024 (Cod: 992067). The authors of the study express their sincere gratitude to all authorities of the Student Research Committee of Mashhad University of Medical Sciences and MS comprehensive center.

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Authors FP, HT, NP, MM and JJ designed the study. FP, HT and NP participated in the conception of the study. FP and HT managed and conducted the statistical analyses and interpreted the data. FP, HT, and NP wrote the first draft and FP, MM, HT and JJ revised it to make the final manuscript. All authors have read and approved the final manuscript.

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Pourhaji, F., Taraghdar, M.M., Peyman, N. et al. Explaining the burden of cultural factors on MS disease: a qualitative study of the experiences of women with multiple sclerosis. BMC Women's Health 24 , 477 (2024). https://doi.org/10.1186/s12905-024-03328-0

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Speaker 1: In this video, we're going to dive into the topic of qualitative coding, which you'll need to understand if you plan to undertake qualitative analysis for any dissertation, thesis, or research project. We'll explain what exactly qualitative coding is, the different coding approaches and methods, and how to go about coding your data step by step. So go ahead, grab a cup of coffee, grab a cup of tea, whatever works for you, and let's jump into it. Hey, welcome to Grad Coach TV, where we demystify and simplify the oftentimes intimidating world of academic research. My name's Emma, and today we're going to explore qualitative coding, an essential first step in qualitative analysis. If you'd like to learn more about qualitative analysis or research methodology in general, we've also got videos covering those topics, so be sure to check them out. I'll include the links below. If you're new to Grad Coach TV, hit that subscribe button for more videos covering all things research related. Also, if you're looking for hands-on help with your qualitative coding, check out our one-on-one coaching services, where we hold your hand through the coding process step by step. Alternatively, if you're looking to fast track your coding, we also offer a professional coding service, where our seasoned qualitative experts code your data for you, ensuring high-quality initial coding. If that sounds interesting to you, you can learn more and book a free consultation at gradcoach.com. All right, with that out of the way, let's get into it. To kick things off, let's start by understanding what a code is. At the simplest level, a code is a label that describes a piece of content. For example, in the sentence, pigeons attacked me and stole my sandwich, you could use pigeons as a code. This code would simply describe that the sentence involves pigeons. Of course, there are many ways you could code this, and this is just one approach. We'll explore the different ways in which you can code later in this video. So, qualitative coding is simply the process of creating and assigning codes to categorize data extracts. You'll then use these codes later down the road to derive themes and patterns for your actual qualitative analysis. For example, thematic analysis or content analysis. It's worth It's worth noting that coding and analysis can take place simultaneously. In fact, it's pretty much expected that you'll notice some themes emerge while you code. That said, it's important to note that coding does not necessarily involve identifying themes. Instead, it refers to the process of labeling and grouping similar types of data, which in turn will make generating themes and analyzing the data more manageable. You might be wondering then, why should I bother with coding at all? Why not just look for themes from the outset? Well, coding is a way of making sure your data is valid. In other words, it helps ensure that your analysis is undertaken systematically, and that other researchers can review it. In the world of research, we call this transparency. In other words, coding is the foundation of high quality analysis, which makes it an essential first step. Right, now that we've got a plain language definition of coding on the table, the next step is to understand what types of coding exist. Let's start with the two main approaches, deductive and inductive coding. With deductive coding, you as the researcher begin with a set of pre-established codes and apply them to your data set, for example, a set of interview transcripts. Inductive coding, on the other hand, works in reverse, as you start with a blank canvas and create your set of codes based on the data itself. In other words, the codes emerge from the data. Let's take a closer look at both of these approaches. With deductive coding, you'll make use of predetermined codes, also called a priori codes, which are developed before you interact with the present data. This usually involves drawing up a set of codes based on a research question or previous research from your literature review. You could also use an existing code set from the codebook of a previous study. For example, if you were studying the eating habits of college students, you might have a research question along the lines of, what foods do college students eat the most? As a result of this research question, you might develop a code set that includes codes such as sushi, pizza, and burgers. You'd then code your data set using only these codes, regardless of what you find in the data. On the upside, the deductive approach allows you to undertake your analysis with a very tightly focused lens and quickly identify relevant data, avoiding distractions and detours. The downside, of course, is that you could miss out on some very valuable insights as a result of this tight predetermined focus. Now let's look at the opposite approach, inductive coding. As I mentioned earlier, this type of coding involves jumping right into the data without predetermined codes and developing the codes based on what you find within the data. For example, if you were to analyze a set of open-ended interview question responses, you wouldn't necessarily know which direction the conversation would flow. If a conversation begins with a discussion of cats, it might go on to include other animals too. And so, you'd add these codes as you progress with your analysis. Simply put, with inductive coding, you go with the flow of the data. Inductive coding is great when you're researching something that isn't yet well understood because the coding derived from the data helps you explore the subject. Therefore, this approach to coding is usually adopted when researchers want to investigate new ideas or concepts or when they want to create new theories. So, as you can see, the inductive and deductive approaches represent two ends of a spectrum, but this doesn't mean that they're mutually exclusive. You can also take a hybrid approach where you utilize a mix of both. For example, if you've got a set of codes you've derived from a literature review or a previous study, in other words, a deductive approach, but you still don't have a rich enough code set to capture the depth of your qualitative data, you can combine deductive and inductive approaches, which we call a hybrid approach. To adopt a hybrid approach, you'll begin your analysis with a set of a priori codes, in other words, a deductive approach, and then add new codes, in other words, an inductive approach, as you work your way through the data. Essentially, the hybrid coding approach provides the best of both worlds, which is why it's pretty common to see this in research. All right, now that we've covered what qualitative coding is and the overarching approaches, let's dive into the actual coding process and look at how to undertake the coding. So, let's take a look at the actual coding process step by step. Whether you adopt an inductive or deductive approach, your coding will consist of two stages, initial coding and line-by-line coding. In the initial coding stage, the objective is to get a general overview of the data by reading through and understanding it. If you're using an inductive approach, this is also where you'll develop an initial set of codes. Then in the second stage, line-by-line coding, you'll delve deeper into the data and organize it into a formalized set of codes. Let's take a look at these stages of qualitative coding in more detail. Stage one, initial coding. The first step of the coding process is to identify the essence of the text and code it accordingly. While there are many qualitative analysis software options available, you can just as easily code text-based data using Microsoft Word's comments feature. In fact, if it's your first time coding, it's oftentimes best to just stick with Word as this eliminates the additional need to learn new software. Importantly, you should avoid the temptation of any sort of automated coding software or service. No matter what promises they make, automated software simply cannot compare to human-based coding as it can't understand the subtleties of language and context. Don't waste your time with this. In all likelihood, you'll just end up having to recode everything yourself anyway. Okay, so let's take a look at a practical example of the coding process. Assume you had the following interview data from two interviewees. In the initial stage of coding, you could assign the code of pets or animals. These are just initial fairly broad codes that you can and will develop and refine later. In the initial stage, broad rough codes are fine. They're just a starting point which you will build onto later when you undertake line-by-line coding. So, at this stage, you're probably wondering how to decide what codes to use, especially when there are so many ways to read and interpret any given sentence. Well, there are a few different coding methods you can adopt and the right method will depend on your research aims and research questions. In other words, the way you code will depend on what you're trying to achieve with your research. Five common methods utilized in the initial coding stage include in vivo coding, process coding, descriptive coding, structural coding, and value coding. These are not the only methods available, but they're a useful starting point. Let's take a look at each of them to understand how and when each method could be useful. Method number one, in vivo coding. When you use in vivo coding, you make use of a participant's own words rather than your interpretation of the data. In other words, you use direct quotes from participants as your codes. By doing this, you'll avoid trying to infer meaning by staying as close to the original phrases and words as possible. In vivo coding is particularly useful when your data are derived from participants who speak different languages or come from different cultures. In cases like these, it's often difficult to accurately infer meaning thanks to linguistic and or cultural differences. For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn't the same in all cultures. Speakers of Aymara view the past as in front of them and the future as behind them. Why? Because the future is unknown. It must be out of sight or behind them. They know what happened in the past so their perspective is that it's positioned in front of them where they can see it. In a scenario like this one, it's not possible to derive the reason for viewing the past as in front and the future as behind without knowing the Aymara culture's perception of time. Therefore, in vivo coding is particularly useful as it avoids interpretation errors. While this case is a unique one, it illustrates the point that different languages and cultures can view the same things very differently, which would have major impacts on your data. Method number two, process coding. Next up, there's process coding, which makes use of action-based codes. Action-based codes are codes that indicate a movement or procedure. These actions are often indicated by gerunds, that is words ending in ing. For example, running, jumping, or singing. Process coding is useful as it allows you to code parts of data that aren't necessarily spoken but that are still important to understand the meaning of the text. For example, you may have action codes such as describing a panda, singing a song, or arguing with a relative. Another example would be if a participant were to say something like, I have no idea where she is. A sentence like this could be interpreted in many different ways depending on the context and movements of the participant. The participant could, for example, shrug their shoulders, which would indicate that they genuinely don't know where the girl is. Alternatively, they could wink, suggesting that they do actually know where the girl is. Simply put, process coding is useful as it allows you to, in a concise manner, identify occurrences in a set of data that are not necessarily spoken and to provide a dynamic account of events. Method number three, descriptive coding. Descriptive coding is a popular coding method that aims to summarize extracts by using a single word that encapsulates the general idea of the data. These words will typically describe the data in a highly condensed manner, which allows you as the researcher to quickly refer to the content. For example, a descriptive code could be food, when coding a video clip that involves a group of people discussing what they ate throughout the day, or cooking, when coding an image showing the steps of a recipe. Descriptive coding is very useful when dealing with data that appear in forms other than text. For example, video clips, sound recordings, or images. It's also particularly useful when you want to organize a large data set by topic area. This makes descriptive coding a popular choice for many research projects. Method number four, structural coding. True to its name, structural coding involves labeling and describing specific structural attributes of the data. Generally, it includes coding according to answers of the questions of who, what, where, and how, rather than the actual topics expressed in the data. For example, if you were coding a collection of dissertations, which would be quite a large data set, structural coding might be useful as you could code according to different sections within each of these documents. Coding what centric labels, such as hypotheses, literature review, and methodology, would help you to efficiently refer to sections and navigate without having to work through sections of data all over again. So, structural coding is useful when you want to access segments of data quickly, and it can help tremendously when you're dealing with large data sets. Structural coding can also be useful for data from open-ended survey questions. This data may initially be difficult to code as they lack the set structure of other forms of data, such as an interview with a strict closed set of questions to be answered. In this case, it would be useful to code sections of data that answer certain questions, such as who, what, where, and how. Method number five, values coding. Last but not least, values-based coding involves coding excerpts that relate to the participant's worldviews. Typically, this type of coding focuses on excerpts that provide insight regarding the values, attitudes, and beliefs of the participants. In practical terms, this means you'd be looking for instances where your participants say things like, I feel, I think that, I need, and it's important that, as these sorts of statements often provide insight into their values, attitudes, and beliefs. Values coding is therefore very useful when your research aims and research questions seek to explore cultural values and interpersonal experiences and actions, or when you're looking to learn about the human experience. All right, so we've looked at five popular methods that can be used in the initial coding stage. As I mentioned, this is not a comprehensive list, so if none of these sound relevant to your project, be sure to look up alternative coding methods to find the right fit for your research aims. The five methods we've discussed allow you to arrange your data so that it's easier to navigate during the next stage, line-by-line coding. While these methods can all be used individually, it's important to know that it's possible, and quite often beneficial, to combine them. For example, when conducting initial coding with interview data, you could begin by using structural coding to indicate who speaks when. Then, as a next step, you could apply descriptive coding so that you can navigate to and between conversation topics easily. As with all design choices, the right method or combination of methods depends on your research aims and research questions, so think carefully about what you're trying to achieve with your research. Then, select the method or methods that make sense in light of that. So, to recap, the aim of initial coding is to understand and familiarize yourself with your data, to develop an initial code set, if you're taking an inductive approach, and to take the first shot at coding your data. Once that's done, you can move on to the next stage, line-by-line coding. Let's do it. Line-by-line coding is pretty much exactly what it sounds like, reviewing your data line-by-line, digging deeper, refining your codes, and assigning additional codes to each line. With line-by-line coding, the objective is to pay close attention to your data, to refine and expand upon your coding, especially when it comes to adopting an inductive approach. For example, if you have a discussion of beverages and you previously just coded this as beverages, you could now go deeper and code more specifically, such as coffee, tea, and orange juice. The aim here is to scratch below the surface. This is the time to get detailed and specific so that you can capture as much richness from the data as possible. In the line-by-line coding process, it's useful to code as much data as possible, even if you don't think you're going to use it. As you go through this process, your coding will become more thorough and detailed, and you'll have a much better understanding of your data as a result of this. This will be incredibly valuable in the analysis phase, so don't cut corners here. Take your time to work through your data line-by-line and apply your mind to see how you refine your coding as much as possible. Keep in mind that coding is an iterative process, which means that you'll move back and forth between interviews or documents to apply the codes consistently throughout your data set. Be careful to clearly define each code and update previously coded excerpts if you adjust or update the definition of any code, or if you split any code into narrower codes. Line-by-line coding takes time, so don't rush it. Be patient and work through your data meticulously to ensure you develop a high-quality code set. Stage three, moving from coding to analysis. Once you've completed your initial and line-by-line coding, the next step is to start your actual qualitative analysis. Of course, the coding process itself will get you in analysis mode, and you'll probably already have some insights and ideas as a result of it, so you should always keep notes of your thoughts as you work through the coding process. When it comes to qualitative data analysis, there are many different methods you can use, including content analysis, thematic analysis, and discourse analysis. The analysis method you adopt will depend heavily on your research aims and research questions. We cover qualitative analysis methods on the Grad Coach blog, so we're not going to go down that rabbit hole here, but we'll discuss the important first steps that build the bridge from qualitative coding to qualitative analysis. So, how do you get started with your analysis? Well, each analysis will be different, but it's useful to ask yourself the following more general questions to get the wheels turning. What actions and interactions are shown in the data? What are the aims of these interactions and excerpts? How do participants interpret what is happening, and how do they speak about it? What does their language reveal? What are the assumptions made by the participants? What are the participants doing? Why do I want to learn about this? What am I trying to find out? As with initial coding and line-by-line coding, your qualitative analysis can follow certain steps. The first two steps will typically be code categorization and theme identification. Let's look at these two steps. Code categorization, which is the first step, is simply the process of reviewing everything you've coded and then creating categories that can be used to guide your future analysis. In other words, it's about bundling similar or related codes into categories to help organize your data effectively. Let's look at a practical example. If you were discussing different types of animals, your codes may include dogs, llamas, and lions. In the process of code categorization, you could label, in other words, categorize these three animals as mammals, whereas you could categorize flies, crickets, and beetles as insects. By creating these code categories, you will be making your data more organized, as well as enriching it so that you can see new connections between different groups of codes. Once you've categorized your codes, you can move on to the next step, which is to identify the themes in your data. Let's look at the theme identification step. From the coding and categorization processes, you'll naturally start noticing themes. Therefore, the next logical step is to identify and clearly articulate the themes in your data set. When you determine themes, you'll take what you've learned from the coding and categorization stages and synthesize it to develop themes. This is the part of the analysis process where you'll begin to draw meaning from your data and produce a narrative. The nature of this narrative will, of course, depend on your research aims, your research questions, and the analysis method you've chosen. For example, content analysis or thematic analysis. So, keep these factors front of mind as you scan for themes, as they'll help you stay aligned with the big picture. All right, now that we've covered both the what and the how of qualitative coding, I want to quickly share some general tips and suggestions to help you optimize your coding process. Let's rapid fire. One, before you begin coding, plan out the steps you'll take and the coding approach and method or methods you'll follow to avoid inconsistencies. Two, when adopting a deductive approach, it's best to use a codebook with detailed descriptions of each code right from the start of the coding process. This will ensure that you apply codes consistently based on their descriptions and will help you keep your work organized. Three, whether you adopt an inductive or deductive approach, keep track of the meanings of your codes and remember to revisit these as you go along. Four, while coding, keep your research aims, research questions, coding methods, and analysis method front of mind. This will help you to avoid directional drift, which happens when coding is not kept consistent. Five, if you're working in a research team with multiple coders, make sure that everyone has been trained and clearly understands how codes need to be assigned. If multiple coders are pulling in even slightly different directions, you will end up with a mess that needs to be redone. You don't want that. So keep these five tips in mind and you'll be on the fast track to coding success. And there you have it, qualitative coding in a nutshell. Remember, as with every design choice in your dissertation, thesis, or research project, your research aims and research questions will have a major influence on how you approach the coding. So keep these two elements front of mind every step of the way and make sure your coding approach and methods align well. If you enjoyed the video, hit the like button and leave a comment if you have any questions. Also, be sure to subscribe to the channel for more research-related content. If you need a helping hand with your qualitative coding or any part of your research project, remember to check out our private coaching service where we work with you on a one-on-one basis, chapter by chapter, to help you craft a winning piece of research. If that sounds interesting to you, book a free consultation with a friendly coach at gradcoach.com. As always, I'll include a link below. That's all for this episode of Grad Coach TV. Until next time, good luck.