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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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 Research : Definition

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

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Qualitative Data Analysis: What is it, Methods + Examples

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

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

What is Qualitative Data Analysis?

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

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

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

Importance of Qualitative Data Analysis

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

In-Depth Understanding

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

Contextual Insight

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

Theory Development

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

Participant Perspectives

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

Exploratory Research

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

Types of Qualitative Data

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

Interviews and Focus Groups

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

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

Observations and Field Notes

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

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

Textual and Visual Data

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

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

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

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

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

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

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

Qualitative Data Analysis Methods and Examples

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

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

Method 1: Content Analysis

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

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

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

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

Benefits & Challenges

There are various advantages to using content analysis:

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

However, keep in mind the challenges that arise:

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

Example of Content Analysis

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

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

Method 2: Thematic Analysis

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

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

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

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

Thematic analysis has various benefits:

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

However, challenges may arise, such as:

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

Example of Thematic Analysis

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

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

Method 3: Narrative Analysis

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

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

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

There are various advantages to narrative analysis:

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

However, difficulties may arise, such as:

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

Example of Narrative Analysis

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

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

Method 4: Grounded Theory Analysis

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

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

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

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

Grounded theory analysis has various benefits:

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

However, challenges might arise with:

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

Example of Grounded Theory Analysis

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

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

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

Method 5: Discourse Analysis

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

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

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

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

There are various advantages of using discourse analysis:

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

However, the following challenges may arise:

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

Example of Discourse Analysis

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

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

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

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

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

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

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Qualitative Data Analysis

Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:

1. Content analysis . This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.

2. Narrative analysis . This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.

3. Discourse analysis . A method of analysis of naturally occurring talk and all types of written text.

4. Framework analysis . This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.

5. Grounded theory . This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.

Qualitative data analysis can be conducted through the following three steps:

Step 1: Developing and Applying Codes . Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.

There are three types of coding:

• Open coding . The initial organization of raw data to try to make sense of it.
• Axial coding . Interconnecting and linking the categories of codes.
• Selective coding . Formulating the story through connecting the categories.

Coding can be done manually or using qualitative data analysis software such as

 NVivo,  Atlas ti 6.0,  HyperRESEARCH 2.8,  Max QDA and others.

When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.

In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.

Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.

The following table contains examples of research titles, elements to be coded and identification of relevant codes:

 Qualitative data coding

Step 2: Identifying themes, patterns and relationships . Unlike quantitative methods , in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.

Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.

Specifically, the most popular and effective methods of qualitative data interpretation include the following:

• Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
• Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
• Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
• Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.

Step 3: Summarizing the data . At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.

It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of qualitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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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Step-by-step definition of qualitative data analysis.

Home » Step-by-step definition of qualitative data analysis

The Systematic Interpretation Process begins a journey into the complex world of qualitative data analysis. When researchers collect data through interviews or open-ended questions, understanding and extracting meaningful insights becomes crucial. This interpretation process serves as the framework through which raw data transforms into valuable information.

In qualitative research, the systematic approach ensures that diverse perspectives are examined rigorously. By following structured steps, researchers can identify patterns, themes, and relationships within the data, enabling better decision-making. Emphasizing reliability and thoroughness, this method enhances the credibility of findings, ultimately leading to actionable insights that drive strategic initiatives.

Understanding Qualitative Data Analysis

Qualitative data analysis involves interpreting non-numeric data, such as interview transcripts or open-ended survey responses. Understanding this process is essential because it uncovers insights into people's thoughts, feelings, and behaviors. To truly grasp qualitative data analysis, one must appreciate the systematic interpretation process that guides researchers in deriving meaning from this data. This journey entails careful organization, coding of themes, and rigorous validation of findings.

The systematic interpretation process unfolds in several key steps. First, researchers collect rich narrative data through interviews or focus groups. Next, they begin coding, where patterns and themes emerge from the text. Subsequently, it's vital to contextualize these findings, ensuring they resonate with broader social phenomena. Finally, researchers validate and refine their conclusions through member checks or peer debriefing. This structured approach to qualitative data analysis is crucial for ensuring credible and actionable insights that can inform decision-making and policy development.

The Essence of the Systematic Interpretation Process

Understanding the essence of the Systematic Interpretation Process is crucial for effective qualitative data analysis. This process begins with the careful examination of data to uncover patterns and themes. By analyzing the information systematically, researchers can draw meaningful conclusions that are both reliable and relevant. Each step in this process builds toward a comprehensive understanding of the data, allowing researchers to articulate insights clearly.

Central to the Systematic Interpretation Process are several key steps. First, familiarize yourself with the data in order to identify initial impressions. Next, categorize data into themes that emerge from your analysis. Then, interpret these themes by relating them back to your research questions. Finally, report your findings with clarity, ensuring that the interpretation reflects the depth of the data. Embracing this systematic approach not only enhances analysis but also promotes transparency in deriving insights.

Tools and Techniques for Qualitative Data Analysis

Qualitative data analysis often requires specific tools and techniques to ensure a systematic interpretation process. Various methods exist, each tailored to different research objectives and contexts. The first step is to gather qualitative data, which can be collected through interviews, focus groups, or observations. Once you have your data, coding it is essential. This involves categorizing segments of text to identify patterns and themes.

Next, software tools can facilitate analysis, making it easier to sort and visualize data. Examples include NVivo and Atlas.ti, which help organize and interpret large volumes of qualitative data efficiently. Finally, presenting findings through narrative descriptions or visual diagrams can enhance understanding. Each technique plays a pivotal role in unveiling insights that guide decision-making. By utilizing these tools and adhering to a systematic interpretation process, researchers can gain valuable insights from their qualitative data.

Step-by-Step Systematic Interpretation Process in Qualitative Data Analysis

To effectively engage with qualitative data, a systematic interpretation process is essential. This process typically involves several key steps that guide researchers from initial data collection to meaningful insights. First, researchers must familiarize themselves with the data, reviewing transcripts or notes to get a general sense of the content. This initial step sets the foundation for deeper analysis.

Next, coding is implemented, which involves categorizing data into themes or concepts. This organizes the material and helps identify patterns or trends relevant to the research question. Once coding is complete, themes are examined more closely. Researchers analyze the coded data, linking themes back to the original context to draw meaningful conclusions. This meticulous process allows for a comprehensive understanding of qualitative data, ensuring that insights derived are not only relevant but also grounded in the evidence collected. Each step is crucial in making informed decisions based on qualitative research findings.

Data Collection Methods and Initial Review

Data collection methods play a vital role in the initial review of qualitative data analysis. This process begins with determining the types of data you intend to gather, such as interviews, focus groups, or observational studies. Each method yields insights that contribute to a comprehensive understanding of participants’ perspectives. Frequent engagement with your data ensures a foundation for a systematic interpretation process, where themes emerge and patterns align with your research goals.

In the initial review phase, the collected data is scrutinized for relevance and accuracy. This involves organizing the data into manageable segments and identifying initial themes that resonate with your research objectives. Engaging in a thorough review allows for the early detection of biases or gaps in the data that must be addressed before deeper analysis. With a clear framework established, your systematic interpretation process becomes not only efficient but also robust, ensuring validity in your findings.

Coding and Theme Development

In the context of qualitative data analysis, the systematic interpretation process involves coding and theme development. Initially, coding serves as a method to categorize data, highlighting significant patterns and insights. Each segment of data is labeled with a code, creating a foundation for deeper analysis. This coding phase can reveal recurring themes essential in understanding the research context.

Following coding, the focus shifts to theme development. Here, researchers search for connections between the various codes identified earlier. These connections form overarching themes that articulate the narrative within the data. This interplay between coding and theme development enhances interpretative clarity, making it easier for teams to visualize insights and actionable conclusions. By prioritizing a well-organized systematic interpretation process, researchers can effectively translate raw qualitative data into meaningful insights that drive decisions and improvements.

Conclusion: Embracing the Systematic Interpretation Process for Effective Qualitative Data Analysis

The Systematic Interpretation Process serves as a cornerstone for effective qualitative data analysis. By systematically breaking down and analyzing data, researchers can uncover deeper insights and themes, leading to more meaningful conclusions. This structured approach fosters clarity and consistency, key components in interpreting complex qualitative findings.

Moreover, embracing this process empowers analysts to remain objective. When insights are derived systematically, the risk of biases and subjective interpretations is significantly reduced. Ultimately, adopting the Systematic Interpretation Process enriches qualitative research, ensuring that the results are not only reliable but also actionable for decision-makers in various fields.

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What Is Qualitative Analysis?

• How It Works

Qualitative Data

• In the Business Context
• Quantitative Analysis
• Qualitative Analysis FAQs
• Fundamental Analysis

Qualitative Analysis

• Valuing a Company: Business Valuation Defined With 6 Methods
• Valuation Analysis
• Financial Statements
• Balance Sheet
• Cash Flow Statement
• 6 Basic Financial Ratios
• 5 Must-Have Metrics for Value Investors
• Earnings Per Share (EPS)
• Price-to-Earnings Ratio (P/E Ratio)
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• Qualitative Analysis CURRENT ARTICLE
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• How to Analyze Corporate Profit Margins
• Return on Equity (ROE)
• Decoding DuPont Analysis
• How to Value Private Companies
• Valuing Startup Ventures

In business and management, qualitative analysis uses subjective judgment to analyze a company's value or prospects based on non-quantifiable information, such as management expertise, industry cycles, strength of research and development, and labor relations.

Qualitative analysis contrasts with  quantitative analysis , which focuses on numbers found in reports such as balance sheets. The two techniques, however, will often be used together to examine a company's operations and evaluate its potential as an investment opportunity.

Key Takeaways

• Qualitative analysis uses subjective judgment based on "soft" or non-quantifiable data.
• Qualitative analysis deals with intangible and inexact information that can be difficult to collect and measure.
• Machines struggle to conduct qualitative analysis as intangibles can’t be defined by numeric values.
• Understanding people and company cultures are central to qualitative analysis.
• Looking at a company through the eyes of a customer and understanding its competitive advantage assists with qualitative analysis.

Investopedia / Paige McLaughlin

Understanding Qualitative Analysis

The distinction between qualitative and quantitative approaches is similar to the difference between human and artificial intelligence. Quantitative analysis uses exact inputs such as profit margins, debt ratios , earnings multiples, and the like. These can be plugged into a computerized model to yield an exact result, such as the fair value  of a stock or a forecast for earnings growth. Of course, for the time being, a human has to write the program that crunches these numbers, and that involves a fair degree of subjective judgment. Once they are programmed, though, computers can perform quantitative analysis in fractions of a second, while it might take even the most gifted and highly-trained humans minutes or hours.

Qualitative analysis, on the other hand, deals with intangible, inexact concerns that belong to the social and experiential realm rather than the mathematical one. This approach depends on the kind of intelligence that machines (currently) lack, since things like positive associations with a brand, management trustworthiness, customer satisfaction, competitive advantage , and cultural shifts are difficult, arguably impossible, to capture with numerical inputs. 

Many social scientists use qualitative analysis in their research, and it is especially prominent among anthropologists and sociologists.

Understanding People Through Qualitative Analysis

Qualitative analysis may sound almost like "listening to your gut," and indeed many qualitative analysts would argue that gut feelings have their place in the process. That does not mean, however, that it is not a rigorous approach. Indeed, it can consume much more time and energy than quantitative analysis.

People are central to qualitative analysis. An investor might start by getting to know a company's management , including their educational and professional backgrounds. One of the most important factors is their experience in the industry. More abstractly, do they have a record of hard work and prudent decision-making, or are they better at knowing—or being related to—the right people? Their reputations are also key: do their colleagues and peers respect them? Their relationships with business partners are also worth exploring since these can have a direct impact on operations.

Company Culture and Qualitative Analysis

The way employees view the company and its management is important. Are they satisfied and motivated, or do they resent their bosses? The rate of employee turnover can indicate employees' loyalty or lack thereof. What does workplace culture say about the company? Overly hierarchical offices promote intrigue and competition and sap productive energy; a sleepy, unmotivated environment can mean employees are mainly concerned with punching the clock. The ideal is a vibrant, creative culture that attracts top talent.

Gathering data for qualitative analysis can sometimes be difficult. Fortune 500 CEOs are not known for sitting down with retail investors for a chat or showing them around the corporate headquarters. In part, Warren Buffett can use qualitative analysis so effectively because people are willing to give him access to their time and information. The rest of us have to sift through news reports and companies' filings to get a sense of managers' records, strategies, and philosophies.

The management discussion and analysis (MD&A) section of a company's 10-K filing and quarterly earnings conference calls provide a window into strategies and communication styles. Clear, transparent communication and coherent strategies are useful. Buzzwords, evasiveness, and short-termism, not so much.

Qualitative data can also be collected in a number of other ways including interviews, panel groups, ethnography (participant observation), archival work, and document analysis. Qualitative data is often read carefully and coded thematically to identify themes, patterns, and trends.

Qualitative Analysis in the Business Context

Customers are the only group more crucial to a company's success than management and employees since they are the source of its revenue. Ironically, if a company places customers' interests before shareholders, it may be a better long-term investment. If feasible, it's a good idea to try being a customer. Say you're considering investing in an airline that has reined in costs, beat earnings estimates in three consecutive quarters, and plans to buy back shares . When you try to actually use the airline, however, you find the website bug-ridden, the customer service representatives cranky, the extra fees petty, and your fellow passengers resentful. The negative experience tells you that the company has a lack of priority for its customers and to be careful making an investment in the airline.

A company's business model and competitive advantage are vital components of qualitative analysis. What gives the firm an enduring leg up over its rivals? Has it invented a new technology that competitors will find hard to replicate, or that has intellectual property protection? Does it have a unique approach to solving a problem for its customers? Is its brand globally recognized—in a good way? Does its product have cultural resonance or an element of nostalgia? Will there still be a market for it in twenty years? If you can plausibly imagine another company stepping in and doing what this one does just a little bit better, then the barrier to entry may be too low. Why will an un-established company be the one to create or disrupt its chosen market, and why won't it then be replaced in turn?

Example of Qualitative Analysis in Business

The idea behind quantitative analysis is to measure things; the idea behind qualitative analysis is to understand them. The latter requires a holistic view and a fact-based overarching narrative. Context is key. For example, a CEO who dropped out of college would be a red flag in some cases, but Mark Zuckerberg and Steve Jobs are exceptions. Silicon Valley is, for better or worse, a different beast. A look at McDonald's Corp's ( MCD ) financials a few years ago would have told you nothing about a looming backlash against cheap, unhealthy food. On the other hand, a purely qualitative approach is vulnerable to distortion by blind spots and personal biases. Quantitative measures can act as a check on these tendencies.

Qualitative Analysis vs. Quantitative Analysis

Qualitative analysis relies on thick description and deep understanding of the subject being researched, obtained from in-depth interviews, observations, and/or close readings of text. This type of research typically looks at case studies and can be used to understand local phenomena.

Quantitative analysis instead relies on the statistical analyses of numerical data obtained from surveys, experiments, or administrative records. From this, inferences can be made and correlations between variables analyzed to understand more generalized phenomena.

What Are the Steps in Qualitative Analysis?

Although the exact steps may vary, most researchers and analysts undertaking qualitative analysis will follow these steps:

• Define your goals and objective
• Collect or obtain qualitative data
• Analyze the data to generate initial topic codes
• Identify patterns or themes in the codes
• Review and revise codes based on initial analysis
• Write up your findings

What Are the Methods of Qualitative Analysis?

Qualitative research encompasses a wide range of techniques and methodologies. Among the most common include:

• Ethnography (participant observation)
• Narrative or discourse analysis
• Focus groups
• Document/archival analysis

What Are Some Examples of Qualitative Data?

Qualitative data can take many forms. Common types include transcripts generated from one-on-one interviews, free text responses on surveys, narratives, quotations, text documents, images, or observations taken down in a notebook or research journal.

Where Is Qualitative Analysis Used?

Qualitative analysis can be applied to a wide range of research topics or practical settings. It is best used if you are interested in understanding human behavior from an informant or participant perspective to get a better understanding of what is going on in the social context around you.

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You’re on a business trip in Oakland, CA. You've been working late in downtown and now you're looking for a place nearby to grab a late dinner. You decided to check Zomato to try and find somewhere to eat. (Don't begin searching yet).

• Look around on the home page. Does anything seem interesting to you?
• How would you go about finding a place to eat near you in Downtown Oakland? You want something kind of quick, open late, not too expensive, and with a good rating.
• What do the reviews say about the restaurant you've chosen?
• What was the most important factor for you in choosing this spot?
• You're currently close to the 19th St Bart station, and it's 9PM. How would you get to this restaurant? Do you think you'll be able to make it before closing time?
• Your friend recommended you to check out a place called Belly while you're in Oakland. Try to find where it is, when it's open, and what kind of food options they have.
• Now go to any restaurant's page and try to leave a review (don't actually submit it).

What was the worst thing about your experience?

It was hard to find the bart station. The collections not being able to be sorted was a bit of a bummer

What other aspects of the experience could be improved?

Feedback from the owners would be nice

What did you like about the website?

The flow was good, lots of bright photos

What other comments do you have for the owner of the website?

I like that you can sort by what you are looking for and i like the idea of collections

You're going on a vacation to Italy next month, and you want to learn some basic Italian for getting around while there. You decided to try Duolingo.

• Please begin by downloading the app to your device.
• Choose Italian and get started with the first lesson (stop once you reach the first question).
• Now go all the way through the rest of the first lesson, describing your thoughts as you go.
• Get your profile set up, then view your account page. What information and options are there? Do you feel that these are useful? Why or why not?
• After a week in Italy, you're going to spend a few days in Austria. How would you take German lessons on Duolingo?
• What other languages does the app offer? Do any of them interest you?

I felt like there could have been a little more of an instructional component to the lesson.

It would be cool if there were some feature that could allow two learners studying the same language to take lessons together. I imagine that their screens would be synced and they could go through lessons together and chat along the way.

Overall, the app was very intuitive to use and visually appealing. I also liked the option to connect with others.

Overall, the app seemed very helpful and easy to use. I feel like it makes learning a new language fun and almost like a game. It would be nice, however, if it contained more of an instructional portion.

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What is Thematic Analysis in Qualitative Research? Definition, Process and Examples and Best Practices

What is Thematic Analysis in Qualitative Research?

Thematic analysis is a widely used method in qualitative research that involves identifying, analyzing, and reporting patterns, themes, or recurring ideas within a dataset. It is a flexible and systematic approach that allows researchers to uncover meaningful insights and understandings from the rich, often narrative data collected in qualitative studies. The goal of thematic analysis is to distill and organize the data into coherent themes that capture the essence of participants’ experiences, perceptions, or perspectives.

The first step in thematic analysis involves familiarizing oneself with the data through a process known as data immersion. Researchers immerse themselves in the raw data, which can include transcripts from interviews, focus group discussions, or any other qualitative material. This immersion helps researchers gain a holistic understanding of the content and identify initial patterns or notable observations.

Once familiarized with the data, the researcher generates initial codes, which are labels or tags attached to segments of the data that represent specific ideas, concepts, or recurring patterns. These codes are then organized into potential themes. Thematic analysis emphasizes a bottom-up, data-driven approach, allowing themes to emerge organically from the data rather than being imposed based on preconceived notions. Through an iterative process of reviewing, refining, and defining themes, researchers aim to create a coherent and internally consistent representation of the underlying patterns within the dataset.

Finally, thematic analysis involves interpreting and reporting the identified themes in a way that captures the essence of the participants’ experiences or perspectives. This process often involves selecting representative quotes or excerpts from the data to illustrate each theme, providing a rich and vivid portrayal of the findings. Thematic analysis is valued for its adaptability across various research designs and its ability to offer valuable insights into the complexities of qualitative data, making it a widely employed method in the social sciences.

Key Characteristics of Thematic Analysis in Qualitative Research

Thematic analysis is a qualitative research method characterized by several key features that guide the systematic examination and interpretation of textual data. These characteristics contribute to the method’s flexibility and applicability across diverse research contexts. Here are key characteristics of thematic analysis:

• Thematic analysis is primarily inductive, meaning that it allows themes to emerge from the data rather than imposing pre-existing theoretical frameworks. It is driven by the participants’ voices and experiences, emphasizing a bottom-up process that captures the richness of the data.
• Thematic analysis is known for its flexibility, making it applicable to various research questions, study designs, and data types. Researchers can tailor the approach to suit the specific needs of their study, whether it involves interviews, focus groups, or other qualitative data sources.
• Before identifying themes, researchers engage in a process of data immersion and familiarization. This involves a thorough review of the raw data to gain a deep understanding of its content. By immersing themselves in the data, researchers can identify patterns, nuances, and potential areas of interest.
• Thematic analysis involves the systematic coding of data, where researchers assign labels or codes to segments of text representing specific ideas or patterns. These codes are then organized into potential themes. The process is iterative, allowing for constant refinement and development of themes as the analysis progresses.
• Thematic analysis encourages reflexivity, prompting researchers to be aware of their own perspectives, biases, and potential influences on the interpretation of data. This self-awareness contributes to transparency and helps ensure that the analysis is grounded in the participants’ experiences rather than the researchers’ preconceptions.
• Themes in thematic analysis are often emergent, arising from the data rather than being predetermined. Additionally, thematic analysis can involve hierarchical organization of themes, where overarching themes encompass sub-themes, providing a layered and nuanced understanding of the data.
• Transparency is a key characteristic of thematic analysis. Researchers are encouraged to document and report the decision-making process, including how themes were identified, refined, and interpreted. This documentation enhances the rigor and credibility of the research.
• Thematic analysis aims to provide a rich and contextual presentation of the findings. This often involves using participants’ own words or representative quotes to illustrate each theme, allowing readers to connect with the experiences and perspectives being portrayed.
• Thematic analysis is an iterative process that involves multiple rounds of coding, theme generation, and refinement. Researchers continuously revisit the data, codes, and themes to ensure a comprehensive and accurate representation of the dataset.
• Thematic analysis is well-suited for a wide range of research questions, making it applicable in disciplines such as psychology, sociology, education, and health sciences. Its adaptability allows researchers to explore diverse phenomena and capture the complexity of human experiences.

These key characteristics collectively make thematic analysis a versatile and robust qualitative research method, providing researchers with a systematic yet adaptable approach for exploring and understanding the nuances embedded in textual data.

Types of Thematic Analysis in Qualitative Research with Examples

Thematic analysis is a flexible qualitative research method, and there are different types or approaches within thematic analysis. Here are three commonly recognized types with corresponding definitions and examples:

• Inductive thematic analysis involves a bottom-up approach where themes emerge directly from the data. Researchers refrain from using pre-existing theoretical frameworks or prior knowledge to guide the analysis, allowing patterns and themes to surface organically through close examination of the data.
• Example: In a study exploring the experiences of cancer survivors, an inductive approach might involve thoroughly reading interview transcripts, coding segments that stand out, and gradually identifying themes that encapsulate common experiences such as resilience, support systems, and coping strategies.
• Deductive thematic analysis takes a more top-down approach, utilizing pre-existing theories or frameworks to guide the identification and interpretation of themes. Researchers start with predefined categories or concepts and then analyze the data with these predetermined themes in mind.
• Example: In a research project informed by a specific psychological theory, such as Maslow’s Hierarchy of Needs, deductive thematic analysis might involve coding data according to categories derived from Maslow’s theory, such as physiological needs, safety, belongingness, esteem, and self-actualization.
• Framework thematic analysis involves combining elements of both inductive and deductive approaches. Researchers begin with a broad coding framework based on the research question or existing literature but remain open to emergent themes as they delve into the data. The initial framework provides a structure that is flexible enough to evolve through the analysis process.
• Example: In a study examining attitudes toward technology use in education, a framework thematic analysis might start with predefined categories like access, pedagogical integration, and student engagement, but also allow for new themes to emerge during the coding process based on unanticipated insights from the participants.
• Critical thematic analysis goes beyond describing patterns in the data and aims to uncover power structures, social inequalities, and ideologies. It involves questioning assumptions, examining discourses, and exploring how language and representations may perpetuate or challenge existing power dynamics.
• Example: In a study on media representations of a marginalized community, critical thematic analysis might involve examining how specific language choices in news articles contribute to the stereotyping or marginalization of that community. Themes could include instances of linguistic bias, stigmatization, or resistance.
• Narrative thematic analysis focuses on the stories people tell and emphasizes the narrative structure of the data. It involves identifying key plot points, character development, and the ways in which individuals construct and convey meaning through storytelling.
• Example: In a research project exploring personal narratives of overcoming adversity, narrative thematic analysis might involve identifying themes related to the story arc, such as challenges faced, turning points, personal growth, and resolutions. This approach allows researchers to understand how individuals make sense of their experiences through storytelling.

These additional types of thematic analysis reflect the method’s adaptability to various research goals and theoretical orientations. Researchers can choose the type of thematic analysis that aligns with their research questions, epistemological stance, and the depth of analysis required to address the complexities inherent in qualitative data.

Best Practices for Thematic Analysis in Qualitative Research

Thematic analysis is a valuable qualitative research method, and employing best practices enhances the rigor, reliability, and validity of the study findings. Here are some best practices for conducting thematic analysis:

• Begin with well-defined research questions or objectives. Clearly articulate what you aim to explore, ensuring that your thematic analysis remains focused and purposeful.
• Develop a systematic and transparent process for conducting thematic analysis. This process should include distinct stages such as data familiarization, coding, theme generation, reviewing, and reporting. A systematic approach enhances the replicability of your study.
• Immerse yourself in the data to gain a deep understanding of its content. Read and re-read the data to identify patterns, recurring ideas, or potential themes. This initial data immersion phase is crucial for generating meaningful codes and themes.
• Conduct in-depth coding of the data. Code segments that capture meaningful concepts or patterns. Ensure that your coding captures both manifest (explicit) and latent (underlying) content in the data.
• Thematic analysis is an iterative process. Refine and revise codes and themes as you progress through the analysis. Regularly revisit the data to ensure that your emerging themes accurately reflect the complexity of the dataset.
• Be reflexive about your role as a researcher. Acknowledge your preconceptions, biases, and potential influence on the analysis. Document your reflexivity in research notes to enhance transparency.
• Establish clear coding guidelines and maintain consistency in coding across the entire dataset. Consistency enhances the reliability of your analysis, especially if multiple researchers are involved.
• Pay attention to negative or deviant cases that may challenge emerging themes. Ensure that your analysis accounts for variations and exceptions in the data, adding nuance to your interpretations.
• Collaboration can improve the credibility of thematic analysis. If possible, involve other researchers in the process, and seek feedback from peers or experts in qualitative research. This external perspective can enhance the robustness of your findings.
• Maintain a comprehensive audit trail documenting your decision-making processes, from coding to theme generation. This trail serves as a record of your analytical choices and enhances the transparency and trustworthiness of your study.
• Consider using qualitative data analysis software to organize and manage your data. Software tools such as NVivo, MAXQDA, or ATLAS.ti can facilitate efficient coding, retrieval, and organization of thematic data.
• Adhere to ethical standards throughout the research process. Obtain informed consent, protect participant confidentiality, and consider the ethical implications of your analysis, especially when exploring sensitive topics.

By adhering to these best practices, researchers can conduct a robust thematic analysis that contributes meaningful insights to the qualitative research literature. These practices enhance the reliability, validity, and transparency of the research process and findings.

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Paramedics’ experiences and observations: work-related emotions and well-being resources during the initial months of the COVID-19 pandemic—a qualitative study

• Henna Myrskykari 1 , 2 &
• Hilla Nordquist 3  

BMC Emergency Medicine volume  24 , Article number:  152 ( 2024 ) Cite this article

Metrics details

As first responders, paramedics are an extremely important part of the care chain. COVID-19 significantly impacted their working circumstances. We examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic.

This qualitative study utilized reflective essay material written by experienced, advanced-level Finnish paramedics ( n  = 30). The essays used in this study were written during the fall of 2020 and reflected the period when Finland had declared a state of emergency (on 17.3.2020) and the Emergency Powers Act was implemented. The data was analyzed using an inductive thematic analysis.

The emotions experienced by the EMS personnel in their new working circumstances formed three themes: (1) New concerns arose that were constantly present; (2) Surviving without proper guidance; and (3) Rapidly approaching breaking point. Three themes were formed from work-related factors that were identified as resources for the well-being of the EMS personnel. These were: (1) A high level of organizational efficiency was achieved; (2) Adaptable EMS operations; and (3) Encouraging atmosphere.

Conclusions

Crisis management practices should be more attentive to personnel needs, ensuring that managerial and psychological support is readily available in crisis situations. Preparedness that ensures effective organizational adaptation also supports personnel well-being during sudden changes in working circumstances.

Peer Review reports

At the onset of the COVID-19 pandemic, healthcare personnel across the globe faced unprecedented challenges. As initial responders in emergency healthcare, paramedics were quickly placed at the front lines of the pandemic, dealing with a range of emergencies in unpredictable conditions [ 1 ]. The pandemic greatly changed the everyday nature of work [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. Those working on the front line were suddenly forced to adjust to personal protective equipment (PPE) requirements [ 9 , 10 ] and rapidly changing instructions that caused significant adjustments to their job description [ 11 , 12 ]. For instance, it has been reported that during the initial stages of the COVID-19 pandemic, Emergency Medical Services (EMS) personnel, including paramedics working in prehospital emergency care, experienced a significant increase in stress [ 10 , 13 ] due to several reasons, such as the lack of protection and support, increased demands, lack of personnel, fear of exposure to COVID-19 during missions, concerns of spreading the virus to family members, and frustration over quickly changing work policies [ 11 , 14 , 15 ].

With the unprecedented challenges posed by the COVID-19 pandemic, some research has been directed toward identifying available resources that help in coping with such situations. For example, Sangal et al. [ 15 ] underscored the association between effective communication and reduced work stress and burnout, and emphasized the critical need for two-way communication, consistent messaging, and the strategic consolidation of information prior to its dissemination. In parallel, Dickson et al. [ 16 ] highlight the pivotal role of leadership strategies in fostering a healthful work environment. These strategies include being relationally engaging, visibly present, open, and caring for oneself and others, while embodying core values such as compassion, empathy, courage, and authenticity. Moreover, Awais et al. [ 14 ] identify essential measures to reduce mental distress and support EMS personnel’s overall well-being in pandemic conditions, such as by providing accessible mental health and peer support, ensuring a transparent information flow, and the implementation of clear, best-practice protocols and guidelines. As a lesson learned from COVID-19, Kihlström et al. (2022) add that crisis communication, flexible working conditions, compensation, and allowing for mistakes should be part of crisis management. They also emphasize the importance of psychological support for employees. [ 12 ]

Overall, the COVID-19 pandemic had a multifaceted impact on EMS personnel, highlighting the necessity for comprehensive support and resilience strategies to safeguard their well-being [ 11 , 17 , 18 ] alongside organizational functions [ 12 , 19 ]. For example, in Finland, it has been noted in the aftermath of COVID-19 that the availability and well-being of healthcare workers are key vulnerabilities of the resilience of the Finnish health system [ 12 ]. Effective preparedness planning and organizational resilience benefit from learning from past events and gaining a deeper understanding of observations across different organizational levels [ 12 , 19 , 20 ]. For these reasons, it is important to study how the personnel experienced the changing working circumstances and to recognize the resources, even unexpected ones, that supported their well-being during the initial phase of the COVID-19 pandemic [ 12 , 19 ].

The aim of this study was to examine the emotions experienced and the resources identified as supportive of work well-being during the initial months of the COVID-19 pandemic, from the perspective of the paramedics. Our research questions were: According to the experiences and observations of paramedics, (1) what kinds of emotions did the EMS personnel experience in the new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic? In this study, emotions are understood as complex responses involving psychological, physiological, and behavioral components, triggered by significant events or situations [ 21 ]. Resources are understood as physical, psychological, social, or organizational aspects of the work that help achieve work goals, reduce demands and associated costs [ 22 ].

Materials and methods

This qualitative study utilized reflective essay material written in the fall of 2020 by experienced, advanced-level paramedics who worked in the Finnish EMS during the early phase of the pandemic, when Finland had declared (March 17, 2020 onward) a state of emergency and implemented the Emergency Powers Act. This allowed for new rules and guidelines from the government to ensure the security of healthcare resources. Some work rules for healthcare personnel changed, and non-urgent services were limited.

Data collection procedures

This study is part of a broader, non-project-based research initiative investigating the work well-being of paramedics from various perspectives, and the data was collected for research purposes from this standpoint. The data collection for this study was conducted at the South-Eastern Finland University of Applied Sciences as part of the Current Issues in EMS Management course. The course participants were experienced, advanced-level Finnish paramedics who were students of the master’s degree program in Development and Management of Emergency Medical Services. A similar data collection method has been utilized in other qualitative studies [for example, 23 , 24 ].

The South-Eastern Finland University of Applied Sciences granted research permission for the data collection on August 20, 2020. The learning platform “Learn” (an adapted version of Moodle [ 25 ]) was used to gather the data. A research notice, privacy statement, and essay writing instructions were published on the platform on August 21, 2020. The paramedics were asked to write about their own experiences and observations regarding how the state of emergency impacted the work well-being of EMS personnel. They were instructed not to use references but only their own reflections. Three guiding questions were asked: “What kind of workloads did EMS personnel experience during the state of emergency?” “How has this workload differed from normal conditions?” and “What effects did this workload have on the well-being of the EMS personnel?”. The assignment did not refer solely to paramedics because the EMS field community may also include individuals with other titles (such as EMS field supervisors or firefighters performing prehospital emergency care); hence the term “EMS personnel” was used.

The essay was part of the mandatory course assignments, but submitting it for research purposes was voluntary. The paramedics were informed that their participation in the study would not affect their course evaluations. They had the freedom to decline, remove parts of, or withdraw the essay before analysis. None of the paramedics exercised these options. They were also informed that the last author removes any identifying details (such as names, places, and organizational descriptions that could reveal their workplace) before sharing the data with other, at the time unnamed, researchers. The last author (female) is a senior researcher specializing in EMS and work well-being topics, a principal lecturer of the respective course, and the head of the respective master’s program, and familiar to all of them through their studies. The paramedics were aware that the essays were graded by the last author on a pass/fail scale as part of the course assessment. However, comprehensive and well-reasoned reflections positively influenced the course grade. The evaluation was not part of this study. The paramedics had the opportunity to ask further questions about the study directly from the last author during and after the essay writing process and the course.

The paramedics wrote the essays between August 23, 2020, and November 30, 2020. Thirty-two paramedics (out of 39) returned their essays using the Learn platform during this timeframe. Thus, seven of the course completions were delayed, and the essays written later were no longer appropriate to include in the data due to the time elapsed since the initial months of the COVID-19 pandemic.

All 32 gave their informed consent for their essays to be included in the study. Essays written by paramedics who had not actively participated in EMS field work during exceptional circumstances were excluded from the material ( n  = 2), because they wrote the essay from a different perspective, as they could not reflect on their own experiences and observations. Thus, a total of 30 essays were included in the study. The total material was 106 pages long and comprised 32,621 words in Finnish.

Study participants

Thirty advanced-level paramedics from Finland participated in this study. They all had a bachelor’s degree in emergency care or nursing with additional emergency care specialization. At the time of the study, they were pursuing their master’s studies. Thirteen of them were women, and seventeen were men. The average age of the participants was 33.5 years among women and 35.9 years among men. Women had an average of 8.7 years of work experience, and men had 8.8 years. All the participating paramedics worked in EMS in different areas across Finland (except northern Finland) during their studies and the early phase of the pandemic.

Data analysis

The data was analyzed with a thematic analysis following the process detailed by Braun & Clarke [ 26 ]. First, the two researchers thoroughly familiarized themselves with the data, and the refined aim and research questions of the study were formulated inductively in collaboration based on the content of the data (see [ 26 ], page 84). After this, a thorough coding process was mainly carried out by the first author (female), who holds a master’s degree, is an advanced-level paramedic who worked in EMS during the pandemic, and at the time of the analysis was pursuing her doctoral studies in a different subject area related to EMS. Generating the initial codes involved making notes of interesting features of anything that stood out or seemed relevant to the research question systematically across the entire dataset. During this process, the original paragraphs and sentences were copied from the essay material into a table in Microsoft Word, with each research question in separate documents and each paragraph or sentence in its own row. The content of these data extracts was then coded in the adjacent column, carefully preserving the original content but in a more concise form. Then, the content was analyzed, and codes were combined to identify themes. After that, the authors reviewed the themes together by moving back and forth between the original material, the data in the Word documents, and the potential themes. During this process, the authors worked closely and refined the themes, allowing them to be separated and combined into new themes. For example, emotions depicting frustration and a shift to indifference formed their own theme in this kind of process. Finally, the themes were defined into main, major and minor themes and named. In the results, the main themes form the core in response to the research questions and include the most descriptions from the data. The major themes are significant but not as central as the main themes. Major themes provide additional depth and context to the results. One minor theme was formed as the analysis process progressed, and it provided valuable insights and details that deepened the response to the research question. All the coded data was utilized in the formed themes. The full content of the themes is reported in the Results section.

The emotions experienced by the EMS personnel in their new working circumstances formed three themes: New concerns arose that were constantly present (main theme); Surviving without proper guidance (major theme); and Rapidly approaching breaking point (major theme) (Fig.  1 ). Work-related factors identified as resources for the well-being of EMS personnel formed three themes: A high level of organizational efficiency was achieved (main theme); Adaptable EMS operations (major theme); and Encouraging atmosphere (minor theme) (Fig.  2 ).

Emotions experienced by the EMS personnel in their new working circumstances

Main theme: New concerns arose that were constantly present

The main theme included several kinds of new concerns. In the beginning, the uncertainty about the virus raised concerns about work safety and the means to prevent the spread of the disease. The initial lack of training and routines led to uncertainty. In addition, the decrease in the number of EMS missions raised fears of units being reduced and unilateral decisions by the management to change the EMS personnel’s work responsibilities. The future was also a source of uncertainty in the early stages. For example, the transition to exceptional circumstances, concerns about management and the supervisors’ familiarity with national guidelines and lack of information related to sickness absence procedures, leave, personal career progression, and even the progress of vaccine development, all contributed to this feeling of uncertainty. The initial uncertainty was described as the most challenging phase, but the uncertainty was also described as long-lasting.

Being on the front line with an unknown, potentially dangerous, and easily transmissible virus caused daily concerns about the personnel’s own health, especially when some patients hid their symptoms. The thought of working without proper PPE was frightening. On the other hand, waiting for a patient’s test result was stressful, as it often resulted in many colleagues being quarantined. A constant concern for the health of loved ones and the fear of contracting the virus and unknowingly bringing it home or transmitting it to colleagues led the EMS personnel to change their behavior by limiting contact.

Being part of a high-risk group , I often wondered , in the case of coronavirus , who would protect me and other paramedics from human vanity and selfishness [of those refusing to follow the public health guidelines]? (Participant 25)

The EMS personnel felt a weight of responsibility to act correctly, especially from the perspective of keeping their skills up to date. The proper selection of PPE and aseptic procedures were significant sources of concern, as making mistakes was feared to lead to quarantine and increase their colleagues’ workloads. At the same time, concerns about the adequacy of PPE weighed on the personnel, and they felt pressure on this matter to avoid wastage of PPEs. The variability in the quality of PPE also caused concerns.

Concerns about acting correctly were also tied to ethical considerations and feelings of inadequacy when the personnel were unable to explain to patients why COVID-19 caused restrictions on healthcare services. The presence of students also provoked such ethical concerns. Recognizing patients’ symptoms correctly also felt distressing due to the immense responsibility. This concern was also closely tied to fear and even made some question their career choices. The EMS personnel were also worried about adequate treatment for the patients and sometimes felt that the patients were left alone at home to cope. A reduction in patient numbers in the early stages of the pandemic raised concerns about whether acutely ill individuals were seeking help. At the same time, the time taken to put on PPE stressed the personnel because it increased delays in providing care. In the early phase of the pandemic, the EMS personnel were stressed that patients were not protected from them.

I’m vexed in the workplace. I felt it was immediately necessary to protect patients from us paramedics as well. It wasn’t specifically called for , mostly it felt like everyone had a strong need to protect themselves. (Participant 30)

All these concerns caused a particularly heavy psychological burden on some personnel. They described feeling more fatigued and irritable than usual. They had to familiarize themselves with new guidelines even during their free time, which was exhausting. The situation felt unjust, and there was a looming fear of the entire healthcare system collapsing. COVID-19 was omnipresent. Even at the base station of the EMS services, movement was restricted and social distancing was mandated. Such segregation, even within the professional community, added to the strain and reduced opportunities for peer support. The EMS personnel felt isolated, and thoughts about changing professions increased.

It was inevitable that the segregation of the work community would affect the community spirit , and a less able work community has a significant impact on the individual level. (Participant 8)

Major theme: Surviving without proper guidance

At the onset of the pandemic, the job description of the EMS personnel underwent changes, and employers could suddenly relocate them to other work. There was not always adequate support for familiarizing oneself with the new roles, leading to a feeling of loss of control. The management was described as commanding and restricting the personnel’s actions. As opportunities to influence one’s work diminished, the sense of job satisfaction and motivation decreased.

Some felt that leadership was inadequate and neglectful, especially when the leaders switched to remote work. The management did not take the situation seriously enough, leaving the EMS personnel feeling abandoned. The lack of consistent leadership and failure to listen to the personnel caused dissatisfaction and reduced occupational endurance. In addition, the reduced contact with colleagues and close ones reduced the amount of peer support. The existing models for psychological support were found to be inadequate.

Particularly in the early stages, guidelines were seen as ambiguous and deficient, causing frustration, irritation, and fear. The guidelines also changed constantly, even daily, and it was felt that the information did not flow properly from the management to the personnel. Changes in protection recommendations also led to skepticism about the correctness of the national guidance, and the lack of consistent guidelines perplexed the personnel. Internalizing the guidelines was not supported adequately, but the necessity to grasp new information was described as immense and cognitively demanding.

At times , it felt like the work was a kind of survival in a jungle of changing instructions , one mission at a time. (Participant 11)

Major theme: Rapidly approaching breaking point

Risking one’s own health at work caused contentious feelings while concurrently feeling angry that management could work remotely. The arrogant behavior of people toward COVID-19 left them frustrated, while the EMS personnel had to limit their contacts and lost their annual leave. There were fears about forced labor.

Incomplete and constantly changing guidelines caused irritation and indifference, as the same tasks had to be performed with different levels of PPE within a short time. Some guidelines were difficult to comply with in practice, which was vexing.

Using a protective mask was described as distressing, especially on long and demanding missions. Communication and operation became more difficult. Some described frustration with cleaning PPE meant for single use.

Ensuring the proper implementation of a work pair’s aseptic and equipment maintenance was burdensome, and explaining and repeating guidelines was exhausting. A feeling of indifference was emphasized toward the end of a long shift.

After the initial stage, many began to slip with the PPE guidelines and found the instructions excessive. COVID-19 information transmitted by the emergency center lost its meaning, and instructions were left unheeded, as there was no energy to believe that the patient would have COVID-19, especially if only a few disease cases had been reported in their area.

It was disheartening to hear personnel being labeled as selfish for demanding higher pay during exceptional circumstances. This lack of recognition eroded professionalism and increased thoughts of changing professions.

However , being a doormat and a human toilet , as well as a lack of appreciation , undermines my professionalism and the prolonged situation has led me to seriously consider a different job , where values other than dedication and constant flexibility carry weight. I have heard similar thoughts from other colleagues. None of us do this for money. (Participant 9)

Work-related factors identified as resources for the well-being of EMS personnel

Main theme: A high level of organizational efficiency was achieved

The main theme held several different efficient functions. In the early stages of the pandemic, some felt that the information flow was active. Organizations informed the EMS personnel about the disease, its spread, and its impact on the workplace and emergency care activities.

Some felt that managers were easily accessible during the pandemic, at least remotely. Some managers worked long days to be able to support their personnel.

The response to hate and uncertainty was that one of the supervisors was always present in the morning and evening meetings. Supervisors worked long hours so as to be accessible via remote access. (Participant 26)

The organizations took effective steps to control infections. Quick access to COVID-19 tests, clear guidelines for taking sick leave, and permission to take sick leave with a low threshold were seen as positive things. The consideration of personnel belonging to risk groups by moving them to other work tasks was also perceived as positive. In addition, efforts were made to prevent the emergence of infection chains by isolating EMS personnel in their own social facilities.

Established guidelines, especially on the correct use of protective measures, made it easier to work. Some mentioned that the guidelines were available in ambulances and on phones, allowing the protection guidelines to be checked before going on a mission.

The employers took into account the need for psychological support in a diverse manner. Some organizations provided psychological support such as peer debriefing activities, talking therapy with mental health professionals, actively inquiring about their personnel’s feelings, and training them as support workers. The pandemic situation also caused organizations to create their own standard operating models to decrease mental load.

Fortunately , the problem has now been addressed actively , as a peer-to-peer defusing model was built up at our workplace during the crisis , and group defusing has started , the purpose of which is to lighten the work-related mental load. (Participant 3)

Major theme: Adaptable EMS operations

There were several different resources that clarified mission activities. The amount of protective and cleaning equipment was ramped up, and the treatment equipment was quickly updated to meet the demands brought about by the pandemic and to enable safety distances for the EMS personnel. In addition, various guidelines were amended to reduce exposure. For example, personnel on the dedicated COVID-19 ambulances were separated to work without physical contact with others, and field supervisors joined the EMS missions less often than before. Moreover, people at the scene were contacted by phone in advance to ensure that there would be no exposure risk, which also allowed other occupational safety risks to be identified. New practices resulted from the pandemic, such as cleaning communication equipment during shift changes and regularly using PPE with infected patients. All of these were seen as positive resources for efficient work.

At the end of each shift , all keys , telephones , etc., were cleaned and handed over to the next shift. This practice was not previously established in our area , but this will become a permanent practice in the future and is perceived by everyone in our work community as a positive thing. (Participant 10)

Some stated that access to PPE was sufficient, especially in areas where the number of COVID-19 infections was low. PPE was upgraded to make it easier to wear. Further, organizations acquired a variety of cleaning equipment to speed up the disinfection of ambulances.

Organizations hired more employees to enable leave and the operation of dedicated COVID-19 ambulances. The overall number of ambulances was also increased. Non-urgent missions were handled through enhanced phone services, reducing the unnecessary exposure of EMS personnel to COVID-19.

Five extra holiday substitutes were hired for EMS so that the employer could guarantee the success of agreed leave , even if the Emergency Preparedness Act had given them opportunities to cancel or postpone it. (Participant 12)

Minor theme: Encouraging atmosphere

Peer support from colleagues, a positive, comfortable, pleasant work environment, and open discussion, as well as smooth cooperation with other healthcare employees were felt to be resources for work well-being by reducing the heavy workload experienced. Due to the pandemic, the appreciation of healthcare was felt to increase slightly, which was identified as a resource.

One factor affecting resilience in the healthcare sector is certainly that in exceptional circumstances , visibility and appreciation have somewhat increased. (Participant 23)

This study examined, according to the experiences and observations of paramedics, (1) what kinds of emotions the Emergency Medical Service (EMS) personnel experienced in their new working circumstances, and (2) what work-related factors became resources for the well-being of EMS personnel during the initial months of the COVID-19 pandemic. Each research question was answered with three themes.

Previous studies have shown that the pandemic increased the workload of paramedics, prompting changes in their operating models and the function of EMS to align with new pandemic-related requirements [ 9 , 27 ]. Initially, the paramedics in the current study described facing unclear and deficient guidelines and feeling obligated to follow instructions without adequate support to internalize them. Constantly changing instructions were linked to negative emotions in various ways. Moreover, the overwhelming flood of information was heavily connected to this, although the information flow was also perceived as a resource, especially when it was timely and well-structured. The study by Sangal et al. [ 15 ] has raised similar observations and points out the importance of paying special attention to the personnel working in the frontline, as in EMS, who might be more heavily impacted by too much information and anxiety about it. They also discovered that three factors are crucial for addressing the challenges of information overload and anxiety: consolidating information before distributing it, maintaining consistent communication, and ensuring communication is two-way. McAlearney et al. [ 11 ] found that first responders, including EMS personnel, reported frustration regarding COVID-19 information because of inconsistencies between sources, misinformation on social media, and the impact of politics. A Finnish study also recognized that health systems were not sufficiently prepared for the flood of information in the current media environment [ 12 ]. Based on these previous results and our findings, it can be concluded that proper implementation of crisis communication should be an integral part of organizations’ preparedness in the future, ensuring that communication effectively supports employee actions in real-life situations. Secondly, this topic highlights the need for precise guidelines and their implementation. With better preparedness, similar chaos could be avoided in the future [ 17 ].

Many other factors also caused changes in work. The EMS mission profile changed [ 3 , 4 , 5 , 6 ], where paramedics in this study saw concerns. To prevent infection risk, the number of pre-arrival calls increased [ 7 ], the duration of EMS missions increased [ 8 , 9 ], and the continuous use of PPE and enhanced hygiene standards imposed additional burdens [ 9 , 10 ]. In Finland, there was no preparedness for the levels of PPE usage required in the early stages of the pandemic [ 12 ]. In this study, paramedics described that working with potentially inadequate PPE caused fear and frustration, which was increased by a lack of training, causing them to feel a great deal of responsibility for acting aseptically and caring for patients correctly. Conversely, providing adequate PPE, information and training has been found to increase the willingness to work [ 28 ] and the sense of safety in working in a pandemic situation [ 29 ], meaning that the role of precise training, operating instructions and leadership in the use of PPE is emphasized [ 30 ].

The paramedics in this study described many additional new concerns in their work, affecting their lives comprehensively. It has been similarly described that the pandemic adversely affected the overall well-being of healthcare personnel [ 31 ]. The restrictions implemented also impacted their leisure time [ 32 ], and the virus caused concerns for their own and their families’ health [ 11 , 28 ]. In line with this, the pandemic increased stress, burnout [ 10 , 33 ], and anxiety among EMS personnel and other healthcare personnel working on the frontline [ 11 , 14 , 34 , 35 ]. These kinds of results underscore the need for adequate guidance and support, a lack of which paramedics reported experiencing in the current study.

Personnel play a crucial role in the efficient operation of an organization and comprise the main identified resource in this study. Previous studies and summaries have highlighted that EMS personnel did not receive sufficient support during the COVID-19 pandemic [ 11 , 14 , 17 , 18 ]. Research has also brought to light elements of adequate support related to the pandemic, such as a review by Dickson et al. [ 16 ] that presents six tentative theories for healthful leadership, all of which are intertwined with genuine encounter, preparedness, and information use. In this current study, the results showed numerous factors related to these contexts that were identified as resources, specifically underlined by elements of caring, effective operational change, knowledge-based actions, and present leadership, similarly described in a study by Eaton-Williams & Williams [ 18 ]. Moreover, the paramedics in our study highlighted the importance of encouragement and identified peer support from colleagues as a resource, which is in line with studies in the UK and Finland [ 12 , 23 , 37 ].

In the early stages of the pandemic, it was noted that the EMS personnel lacked adequate training to manage their mental health, and there was a significant shortage of psychosocial support measures [ 14 ], although easy access to support would have been significant [ 18 ]. In the current study, some paramedics felt that mental health support was inadequate and delayed, while others observed an increase in mental health support during the pandemic, seeing it as an incentive for organizations to develop standard operating models for mental support, for example. This awakening was identified as a resource. This is consistent, as providing psychological support to personnel has been highlighted as a core aspect of crisis management in a Finnish study assessing health system resilience related to COVID-19 [ 12 ]. In a comprehensive recommendation commentary, Isakov et al. [ 17 ] suggest developing a national strategy to improve resilience by addressing the mental health consequences of COVID-19 and other occupational stressors for EMS personnel. This concept, applicable beyond the US, supports the view that EMS organizations are becoming increasingly aware of the need to prepare for and invest in this area.

A fundamental factor likely underlying all the described emotions was that changes in the job descriptions of the EMS personnel due to the pandemic were significant and, in part, mandated from above. In this study, paramedics described feelings of concern and frustration related to these many changes and uncertainties. According to Zamoum and Gorpe (2018), efficient crisis management emphasizes the importance of respecting emotions, recognizing rights, and making appropriate decisions. Restoring trust is a significant challenge in a crisis situation, one that cannot be resolved without complete transparency and open communication [ 38 ]. This perspective is crucial to consider in planning for future preparedness. Overall, the perspective of employee rights and obligations in exceptional circumstances has been relatively under-researched, but in Australia, grounding research on this perspective has been conducted with paramedics using various approaches [ 39 , 40 , 41 ]. The researchers conclude that there is a lack of clarity about the concept of professional obligation, specifically regarding its boundaries, and the issue urgently needs to be addressed by developing clear guidelines that outline the obligation to respond, both in normal day-to-day operations and during exceptional circumstances [ 39 ].

Complex adaptive systems (CAS) theory recognizes that in a resilient organization, different levels adapt to changing environments [ 19 , 20 ]. Barasa et al. (2018) note that planned resilience and adaptive resilience are both important [ 19 ]. Kihlström et al. (2022) note that the health system’s resilience was strengthened by a certain expectation of crisis, and they also recognized further study needs on how effectively management is responding to weak signals [ 12 ]. This could be directly related to how personnel can prepare for future changes. The results of this study revealed many negative emotions related to sudden changes, but at the same time, effective organizational adaptation was identified as a resource for the well-being of EMS personnel. Dissecting different elements of system adaptation in a crisis has been recognized as a highly necessary area for further research [ 20 ]. Kihlström et al. (2022) emphasize the importance of ensuring a healthy workforce across the entire health system. These frameworks suggest numerous potential areas for future research, which would also enhance effective preparedness [ 12 ].

Limitations of the study

In this study, we utilized essay material written in the fall of 2020, in which experienced paramedics reflected on the early stages of the COVID-19 pandemic from a work-oriented perspective. The essays were approached inductively, meaning that they were not directly written to answer our research questions, but the aim and the research questions were shaped based on the content [ 26 ]. The essays included extensive descriptions that aligned well with the aim of this study. However, it is important to remember when interpreting the results that asking specifically about this topic, for instance, in an interview, might have yielded different descriptions. It can be assessed that the study achieved a tentative descriptive level, as the detailed examination of complex phenomena such as emotions and resources would require various methods and observations.

Although the essays were mostly profound, well-thought-out, and clearly written, their credibility [ 42 ] may be affected by the fact that several months had passed between the time the essays were written and the events described. Memories may have altered, potentially influencing the content of the writings. Diary-like material from the very onset of the pandemic might have yielded more precise data, and such a data collection method could be considered in future research on exceptional circumstances.

The credibility [ 42 ] could also have been enhanced if the paramedics who wrote the essays had commented on the results and provided additional perspectives on the material and analysis through a multi-phase data collection process. This was not deemed feasible in this study, mainly because there was a 2.5-year gap between data collection and the start of the analysis. However, this also strengthened the overall trustworthiness of the study, as it allowed the first author, who had worked in prehospital emergency care during the initial phase of the pandemic, to maintain a distance from the subject, and enabled a comparison of our own findings with previously published research that investigated the same period in different contexts. The comparison was made when writing the discussion, with the analysis itself being inductive and following the thematic analysis process described by Braun & Clarke [ 26 ].

When evaluating credibility [ 42 ], it should also be noted that the participants who wrote the essays, i.e., the data for the study, were experienced paramedics but also students and one of the researchers was their principal lecturer. This could potentially limit credibility if the students, for some reason, did not want to produce truthful content for their lecturer to read. However, this risk can be considered small because the essays’ topics did not concern the students’ academic progress, the essays’ content was quite consistent, and the results aligned with other studies. As a strength, it can be considered that the students shared their experiences without holding back, as the thoughts were not for workplace use, and they could trust the data privacy statement.

To enhance transferability [ 42 ], the context of the study was described in detail, highlighting the conditions prevailing in Finnish prehospital emergency care during the early stages of the pandemic. Moreover, including a diverse range of perspectives from paramedics working in different regions of Finland (except Northern Finland) contributes to the transferability of the study, indicating that the results may be applicable and relevant to a wider context beyond a single specific region.

Dependability [ 42 ] was reinforced by the close involvement of two researchers from different backgrounds in the analysis of the material, but a limitation is that no separate analyses were conducted. However, the original data was repeatedly revisited during the analysis, which strengthened the dependability. Moreover, the first author kept detailed notes throughout the analysis process, and the last author supervised the progress while also contributing to the analysis and reporting. The research process is also reported in detail.

This study highlighted numerous, mainly negative emotions experienced by EMS personnel during the initial months of the COVID-19 pandemic due to new working circumstances. At the same time, several work-related factors were identified as resources for their well-being. The findings suggest that crisis management practices should be more attentive to personnel needs, ensuring that personnel have the necessary support, both managerial and psychological, readily available in crisis situations. Effective organizational adaptation in a crisis situation also supports personnel well-being, emphasizing the importance of effective preparedness. Future research should particularly focus on considering personnel well-being as part of organizational adaptation during exceptional circumstances and utilize these findings to enhance preparedness.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the inclusion of sensitive information and the extent of the informed consent provided by the participants.

Abbreviations

Complex Adaptive Systems (theory)

Coronavirus Disease 2019

Emergency Medical Services

Personal Protective Equipment

United Kingdom

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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

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.

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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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Department of Professional Development and Continuing Education, Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan, Malaysia

Seyedali Ahrari

Department of Counsellor Education and Counselling Psychology, Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan, Malaysia

Noor Syamilah Zakaria

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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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Investigating the effectiveness of endogenous and exogenous drivers of the sustainability (re)orientation of family smes in slovenia: qualitative content analysis approach.

1. Introduction

2. literature review, 2.1. legal framework on sustainable corporate governance (with a focus on smes), 2.1.1. corporate sustainability reporting directive, 2.1.2. corporate sustainability due diligence directive, 2.1.3. scope of the csddd for smes, 2.2. drivers of the family businesses’ (re)orientation towards sustainability, 2.3. endogenous drivers, 2.3.1. the protection of sew, 2.3.2. ownership and management composition, 2.3.3. values, beliefs and attitudes of family owner-managers, 2.3.4. transgenerational continuity and long-term orientation, 2.3.5. knowledge of sustainability, 2.4. exogenous drivers, 2.4.1. stakeholders pressure, 2.4.2. the impact of institutional environment and local communities, 3. empirical research, 3.1. institutional context of slovenia, 3.2. research method, 3.3. sampling and data collection, 3.4. data analysis, 4.1. results of the final coding of the family businesses’ sustainability (re)orientation, 4.2. references to responsibility, preserving (natural) environment and sustainability/sustainable development in the analysed statements, 4.3. family businesses with a higher level of sustainability awareness and orientation, 5. discussion, 5.1. sustainability awareness and readiness of investigated family smes to comply with the new eu legal framework, 5.2. the effectiveness of endogenous and exogenous drivers of family businesses’ sustainability (re)orientation, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Duh, M.; Primec, A. Investigating the Effectiveness of Endogenous and Exogenous Drivers of the Sustainability (Re)Orientation of Family SMEs in Slovenia: Qualitative Content Analysis Approach. Sustainability 2024 , 16 , 7285. https://doi.org/10.3390/su16177285

Duh M, Primec A. Investigating the Effectiveness of Endogenous and Exogenous Drivers of the Sustainability (Re)Orientation of Family SMEs in Slovenia: Qualitative Content Analysis Approach. Sustainability . 2024; 16(17):7285. https://doi.org/10.3390/su16177285

Duh, Mojca, and Andreja Primec. 2024. "Investigating the Effectiveness of Endogenous and Exogenous Drivers of the Sustainability (Re)Orientation of Family SMEs in Slovenia: Qualitative Content Analysis Approach" Sustainability 16, no. 17: 7285. https://doi.org/10.3390/su16177285

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Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo

MSc, PhD, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers ‘cut and paste’ and use coloured pens to categorise data. Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. A number of computer software packages has been developed to mechanise this ‘coding’ process as well as to search and retrieve data. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described.

QUALITATIVE RESEARCH IN MEDICINE

Qualitative research has seen an increased popularity in the last two decades and is becoming widely accepted across a wide range of medical and health disciplines, including health services research, health technology assessment, nursing, and allied health. 1 There has also been a corresponding rise in the reporting of qualitative research studies in medical and health related journals. 2

The increasing popularity of qualitative methods is a result of failure of quantitative methods to provide insight into in-depth information about the attitudes, beliefs, motives, or behaviours of people, for example in understanding the emotions, perceptions and actions of people who suffer from a medical condition. Qualitative methods explore the perspective and meaning of experiences, seek insight and identify the social structures or processes that explain people”s behavioural meaning. 1 , 3 Most importantly, qualitative research relies on extensive interaction with the people being studied, and often allows researchers to uncover unexpected or unanticipated information, which is not possible in the quantitative methods. In medical research, it is particularly useful, for example, in a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods. 4

ANALYSING QUALITATIVE DATA

Qualitative research yields mainly unstructured text-based data. These textual data could be interview transcripts, observation notes, diary entries, or medical and nursing records. In some cases, qualitative data can also include pictorial display, audio or video clips (e.g. audio and visual recordings of patients, radiology film, and surgery videos), or other multimedia materials. Data analysis is the part of qualitative research that most distinctively differentiates from quantitative research methods. It is not a technical exercise as in quantitative methods, but more of a dynamic, intuitive and creative process of inductive reasoning, thinking and theorising. 5 In contrast to quantitative research, which uses statistical methods, qualitative research focuses on the exploration of values, meanings, beliefs, thoughts, experiences, and feelings characteristic of the phenomenon under investigation. 6

Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, observation notes, or other non-textual materials that the researcher accumulates to increase the understanding of the phenomenon. 7 The process of analysing qualitative data predominantly involves coding or categorising the data. Basically it involves making sense of huge amounts of data by reducing the volume of raw information, followed by identifying significant patterns, and finally drawing meaning from data and subsequently building a logical chain of evidence. 8

Coding or categorising the data is the most important stage in the qualitative data analysis process. Coding and data analysis are not synonymous, though coding is a crucial aspect of the qualitative data analysis process. Coding merely involves subdividing the huge amount of raw information or data, and subsequently assigning them into categories. 9 In simple terms, codes are tags or labels for allocating identified themes or topics from the data compiled in the study. Traditionally, coding was done manually, with the use of coloured pens to categorise data, and subsequently cutting and sorting the data. Given the advancement of software technology, electronic methods of coding data are increasingly used by qualitative researchers.

Nevertheless, the computer does not do the analysis for the researchers. Users still have to create the categories, code, decide what to collate, identify the patterns and draw meaning from the data. The use of computer software in qualitative data analysis is limited due to the nature of qualitative research itself in terms of the complexity of its unstructured data, the richness of the data and the way in which findings and theories emerge from the data. 10 The programme merely takes over the marking, cutting, and sorting tasks that qualitative researchers used to do with a pair of scissors, paper and note cards. It helps to maximise efficiency and speed up the process of grouping data according to categories and retrieving coded themes. Ultimately, the researcher still has to synthesise the data and interpret the meanings that were extracted from the data. Therefore, the use of computers in qualitative analysis merely made organisation, reduction and storage of data more efficient and manageable. The qualitative data analysis process is illustrated in Figure 1 .

Qualitative data analysis flowchart

USING NVIVO IN QUALITATIVE DATA ANALYSIS

NVivo is one of the computer-assisted qualitative data analysis softwares (CAQDAS) developed by QSR International (Melbourne, Australia), the world’s largest qualitative research software developer. This software allows for qualitative inquiry beyond coding, sorting and retrieval of data. It was also designed to integrate coding with qualitative linking, shaping and modelling. The following sections discuss the fundamentals of the NVivo software (version 2.0) and illustrates the primary tools in NVivo which assist qualitative researchers in managing their data.

Key features of NVivo

To work with NVivo, first and foremost, the researcher has to create a Project to hold the data or study information. Once a project is created, the Project pad appears ( Figure 2 ). The project pad of NVivo has two main menus: Document browser and Node browser . In any project in NVivo, the researcher can create and explore documents and nodes, when the data is browsed, linked and coded. Both document and node browsers have an Attribute feature, which helps researchers to refer the characteristics of the data such as age, gender, marital status, ethnicity, etc.

Project pad with documents tab selected

The document browser is the main work space for coding documents ( Figure 3 ). Documents in NVivo can be created inside the NVivo project or imported from MS Word or WordPad in a rich text (.rtf) format into the project. It can also be imported as a plain text file (.txt) from any word processor. Transcripts of interview data and observation notes are examples of documents that can be saved as individual documents in NVivo. In the document browser all the documents can be viewed in a database with short descriptions of each document.

Document browser with coder and coding stripe activated

NVivo is also designed to allow the researcher to place a Hyperlink to other files (for example audio, video and image files, web pages, etc.) in the documents to capture conceptual links which are observed during the analysis. The readers can click on it and be taken to another part of the same document, or a separate file. A hyperlink is very much like a footnote.

The second menu is Node explorer ( Figure 4 ), which represents categories throughout the data. The codes are saved within the NVivo database as nodes. Nodes created in NVivo are equivalent to sticky notes that the researcher places on the document to indicate that a particular passage belongs to a certain theme or topic. Unlike sticky notes, the nodes in NVivo are retrievable, easily organised, and give flexibility to the researcher to either create, delete, alter or merge at any stage. There are two most common types of node: tree nodes (codes that are organised in a hierarchical structure) and free nodes (free standing and not associated with a structured framework of themes or concepts). Once the coding process is complete, the researcher can browse the nodes. To view all the quotes on a particular Node, select the particular node on the Node Explorer and click the Browse button ( Figure 5 ).

Node explorer with a tree node highlighted

Browsing a node

Coding in NVivo using Coder

Coding is done in the document browser. Coding involves the desegregation of textual data into segments, examining the data similarities and differences, and grouping together conceptually similar data in the respective nodes. 11 The organised list of nodes will appear with a click on the Coder button at the bottom of document browser window.

To code a segment of the text in a project document under a particular node, highlight the particular segment and drag the highlighted text to the desired node in the coder window ( Figure 3 ). The segments that have been coded to a particular node are highlighted in colours and nodes that have attached to a document turns bold. Multiple codes can be assigned to the same segment of text using the same process. Coding Stripes can be activated to view the quotes that are associated with the particular nodes. With the guide of highlighted text and coding stripes, the researcher can return to the data to do further coding or refine the coding.

Coding can be done with pre-constructed coding schemes where the nodes are first created using the Node explorer followed by coding using the coder. Alternatively, a bottom-up approach can be used where the researcher reads the documents and creates nodes when themes arise from the data as he or she codes.

Making and using memos

In analysing qualitative data, pieces of reflective thinking, ideas, theories, and concepts often emerge as the researcher reads through the data. NVivo allows the user the flexibility to record ideas about the research as they emerge in the Memos . Memos can be seen as add-on documents, treated as full status data and coded like any other documents. 12 Memos can be placed in a document or at a node. A memo itself can have memos (e.g. documents or nodes) linked to it, using DocLinks and NodeLinks .

Creating attributes

Attributes are characteristics (e.g. age, marital status, ethnicity, educational level, etc.) that the researcher associates with a document or node. Attributes have different values (for example, the values of the attribute for ethnicity are ‘Malay’, ‘Chinese’ and ‘Indian’). NVivo makes it possible to assign attributes to either document or node. Items in attributes can be added, removed or rearranged to help the researcher in making comparisons. Attributes are also integrated with the searching process; for example, linking the attributes to documents will enable the researcher to conduct searches pertaining to documents with specified characteristics ( Figure 6 ).

Document attribute explorer

Search operation

The three most useful types of searches in NVivo are Single item (text, node, or attribute value), Boolean and Proximity searches. Single item search is particularly important, for example, if researchers want to ensure that every mention of the word ‘cure’ has been coded under the ‘Curability of cervical cancer’ tree node. Every paragraph in which this word is used can be viewed. The results of the search can also be compiled into a single document in the node browser and by viewing the coding stripe. The researcher can check whether each of the resulting passages has been coded under a particular node. This is particularly useful for the researcher to further determine whether conducting further coding is necessary.

Boolean searches combine codes using the logical terms like ‘and’, ‘or’ and ‘not’. Common Boolean searches are ‘or’ (also referred to as ‘combination’ or ‘union’) and ‘and’ (also called ‘intersection’). For example, the researcher may wish to search for a node and an attributed value, such as ‘ever screened for cervical cancer’ and ‘primary educated’. Search results can be displayed in matrix form and it is possible for the researcher to perform quantitative interpretations or simple counts to provide useful summaries of some aspects of the analysis. 13 Proximity searches are used to find places where two items (e.g. text patterns, attribute values, nodes) appear near each other in the text.

Using models to show relationships

Models or visualisations are an essential way to describe and explore relationships in qualitative research. NVivo provides a Modeler designated for visual exploration and explanation of relationships between various nodes and documents. In Model Explorer, the researcher can create, label and connect ideas or concepts. NVivo allows the user to create a model over time and have any number of layers to track the progress of theory development to enable the researcher to examine the stages in the model-building over time ( Figure 7 ). Any documents, nodes or attributes can be placed in a model and clicking on the item will enable the researcher to inspect its properties.

Model explorer showing the perceived risk factors of cervical cancer

NVivo has clear advantages and can greatly enhance research quality as outlined above. It can ease the laborious task of data analysis which would otherwise be performed manually. The software certainly removes the tremendous amount of manual tasks and allows more time for the researcher to explore trends, identify themes, and make conclusions. Ultimately, analysis of qualitative data is now more systematic and much easier. In addition, NVivo is ideal for researchers working in a team as the software has a Merge tool that enables researchers that work in separate teams to bring their work together into one project.

The NVivo software has been revolutionised and enhanced recently. The newly released NVivo 7 (released March 2006) and NVivo 8 (released March 2008) are even more sophisticated, flexible, and enable more fluid analysis. These new softwares come with a more user-friendly interface that resembles the Microsoft Windows XP applications. Furthermore, they have new data handling capacities such as to enable tables or images embedded in rich text files to be imported and coded as well. In addition, the user can also import and work on rich text files in character based languages such as Chinese or Arabic.

To sum up, qualitative research undoubtedly has been advanced greatly by the development of CAQDAS. The use of qualitative methods in medical and health care research is postulated to grow exponentially in years to come with the further development of CAQDAS.

More information about the NVivo software

Detailed information about NVivo’s functionality is available at http://www.qsrinternational.com . The website also carries information about the latest versions of NVivo. Free demonstrations and tutorials are available for download.

ACKNOWLEDGEMENT

The examples in this paper were adapted from the data of the study funded by the Ministry of Science, Technology and Environment, Malaysia under the Intensification of Research in Priority Areas (IRPA) 06-02-1032 PR0024/09-06.

TERMINOLOGY

Attributes : An attribute is a property of a node, case or document. It is equivalent to a variable in quantitative analysis. An attribute (e.g. ethnicity) may have several values (e.g. Malay, Chinese, Indian, etc.). Any particular node, case or document may be assigned one value for each attribute. Similarities within or differences between groups can be identified using attributes. Attribute Explorer displays a table of all attributes assigned to a document, node or set.

CAQDAS : Computer Aided Qualitative Data Analysis. The CAQDAS programme assists data management and supports coding processes. The software does not really analyse data, but rather supports the qualitative analysis process. NVivo is one of the CAQDAS programmes; others include NUDIST, ATLAS-ti, AQUAD, ETHNOGRAPH and MAXQDA.

Code : A term that represents an idea, theme, theory, dimension, characteristic, etc., of the data.

Coder : A tool used to code a passage of text in a document under a particular node. The coder can be accessed from the Document or Node Browser .

Coding : The action of identifying a passage of text in a document that exemplifies ideas or concepts and connecting it to a node that represents that idea or concept. Multiple codes can be assigned to the same segment of text in a document.

Coding stripes : Coloured vertical lines displayed at the right-hand pane of a Document ; each is named with title of the node at which the text is coded.

DataLinks : A tool for linking the information in a document or node to the information outside the project, or between project documents. DocLinks , NodeLinks and DataBite Links are all forms of DataLink .

Document : A document in an NVivo project is an editable rich text or plain text file. It may be a transcription of project data or it may be a summary of such data or memos, notes or passages written by the researcher. The text in a document can be coded, may be given values of document attributes and may be linked (via DataLinks ) to other related documents, annotations, or external computer files. The Document Explorer shows the list of all project documents.

Memo : A document containing the researcher”s commentary flagged (linked) on any text in a Document or Node. Any files (text, audio or video, or picture data) can be linked via MemoLink .

Model : NVivo models are made up of symbols, usually representing items in the project, which are joined by lines or arrows, designed to represent the relationship between key elements in a field of study. Models are constructed in the Modeller .

Node : Relevant passages in the project”s documents are coded at nodes. A Node represents a code, theme, or idea about the data in a project. Nodes can be kept as Free Nodes (without organisation) or may be organised hierarchically in Trees (of categories and subcategories). Free nodes are free-standing and are not associated to themes or concepts. Early on in the project, tentative ideas may be stored in the Free Nodes area. Free nodes can be kept in a simple list and can be moved to a logical place in the Tree Node when higher levels of categories are discovered. Nodes can be given values of attributes according to the features of what they represent, and can be grouped in sets. Nodes can be organised (created, edited) in Node Explorer (a window listing all the project nodes and node sets). The Node Browser displays the node”s coding and allow the researcher to change the coding.

Project : Collection of all the files, documents, codes, nodes, attributes, etc. associated with a research project. The Project pad is a window in NVivo when a project is open which gives access to all the main functions of the programme.

Sets : Sets in NVivo hold shortcuts to any nodes or documents, as a way of holding those items together without actually combining them. Sets are used primarily as a way of indicating items that in some way are related conceptually or theoretically. It provides different ways of sorting and managing data.

Tree Node : Nodes organised hierarchically into trees to catalogue categories and subcategories.

• Open access
• Published: 19 August 2024

Updating a conceptual model of effective symptom management in palliative care to include patient and carer perspective: a qualitative study

• Emma J. Chapman 1 ,
• Carole A. Paley 1 ,
• Simon Pini 2 &
• Lucy E. Ziegler 1  

BMC Palliative Care volume  23 , Article number:  208 ( 2024 ) Cite this article

128 Accesses

Metrics details

A conceptual model of effective symptom management was previously developed from interviews with multidisciplinary healthcare professionals (HCP) working in English hospices. Here we aimed to answer the question; does a HCP data-derived model represent the experience of patients and carers of people with advanced cancer?

Semi-structured interviews were undertaken with six patients with advanced cancer and six carers to gain an in-depth understanding of their experience of symptom management. Analysis was based on the framework method; transcription, familiarisation, coding, applying analytical framework (conceptual model), charting, interpretation. Inductive framework analysis was used to align data with themes in the existing model. A deductive approach was also used to identify new themes.

The experience of patients and carers aligned with key steps of engagement, decision making, partnership and delivery in the HCP-based model. The data aligned with 18 of 23 themes. These were; Role definition and boundaries, Multidisciplinary team decision making, Availability of services/staff, Clinician-Patient relationship/rapport, Patient preferences, Patient characteristics, Quality of life versus treatment need, Staff time/burden, Psychological support -informal, Appropriate understanding, expectations, acceptance and goals- patients, Appropriate understanding, expectations, acceptance and goals-HCPs, Appropriate understanding, expectations, acceptance and goals- family friends, carers, Professional, service and referral factors, Continuity of care, Multidisciplinary team working, Palliative care philosophy and culture, Physical environment and facilities, Referral process and delays. Four additional patient and carer-derived themes were identified: Carer Burden, Communication, Medicines management and COVID-19. Constructs that did not align were Experience (of staff), Training (of staff), Guidelines and evidence, Psychological support (for staff) and Formal psychological support (for patients).

Conclusions

A healthcare professional-based conceptual model of effective symptom management aligned well with the experience of patients with advanced cancer and their carers. Additional domains were identified. We make four recommendations for change arising from this research. Routine appraisal and acknowledgement of carer burden, medicine management tasks and previous experience in healthcare roles; improved access to communication skills training for staff and review of patient communication needs. Further research should explore the symptom management experience of those living alone and how these people can be better supported.

Peer Review reports

A conceptual model of effective symptom management was previously developed from qualitative data derived from interviews with healthcare professionals working in English hospices to elicit their views about the barriers and facilitators of effective symptom management [ 1 ]. The model delineated the successful symptom management experience into four steps of: engagement, decision-making, partnership and delivery. Constructs contributing to these were identified (Table 1 ).

Our original model was based solely on Healthcare professional (HCP) input. However, the perception of professionals may vary from that of patients and carers. A recent patient and professional survey of needs assessments in an oncology inpatient unit showed discrepancies between perception of unmet needs between staff and patients [ 2 ]. For this reason, we were concerned that what was deemed important by HCP working in palliative care may not mirror the concerns and experience of patients and carers.

Here we aimed to answer the question; does an HCP data-derived model represent the experience of patients and carers of people with advanced cancer?. If necessary, the original conceptual model of effective symptom management will be updated.

Qualitative, semi-structured interviews were chosen to gain an in-depth understanding of the experience from the perspective of a range of patients and carers. All methods were carried out in accordance with the principles of the Declaration of Helsinki. Ethical approval was granted by a UK research ethics committee ( North of Scotland [ 2 ] Research Ethics Committee (20/NS/0086)). Verbal, recorded informed consent was given using a verbal consent script (Supplementary information 1). Our original intention had been to conduct interviews face to face facilitated by a set of laminated prompt cards based upon those used in the HCP interviews. However, adaptation to telephone interviews in patient’s homes was necessary due to COVID-19 restrictions and it became apparent that the card exercise did not work well remotely. We continued interviews based on the interview schedule but without the use of prompt cards. EC is a female, non-clinical senior research fellow in palliative care. She has experience of qualitative interviews and led the development of the original HCP-based model of effective symptom management [ 1 ]. Audio recordings were transcribed verbatim by a senior academic secretary.

Recruitment

Participants who met the inclusion criteria were identified by a research nurse at the participating hospice. Eligible patients were those who met all 5 criteria:

Diagnosed with advanced disease (i.e., cancer that is considered to be incurable).

Had been referred to the participating hospice.

Were 18 years of age or over.

Were able to speak and understand English.

Were able to give informed consent.

Eligible carers were people who met all 4 criteria:

Were the informal carer of an eligible patient (who may or may not also be participating in the study).

Patients or carers were excluded if they:

Exhibited cognitive dysfunction which would impede their being able to give informed consent and take part in the study.

Were deemed by hospice staff to be too ill or distressed.

Access to the inpatient unit was not possible at this time due to Covid-19 restrictions. The research nurse introduced the study, provided a participant information sheet and completed a consent to contact form. The first contact with the researcher was made by telephone to confirm (or not) interest in participation and answer questions. An interview time not less than 48 h after provision of the participant information sheet, was scheduled. The researcher and the participant information sheet explained the overall aim of the RESOLVE research programme to improve health status and symptom experience for people living with advanced cancer (Supplementary information 2). The verbal consent statements made it clear that this was a conversation for research purposes only and would not have any impact on the care the patient received (Supplementary information 3). Permission was granted that the researcher may contact the clinical team at the hospice if there was a serious concern for welfare that required urgent attention. Verbal informed consent was collected, and audio recorded at the start of the interview with participants answering yes or no to each of the statements in the verbal consent script (Supplementary information 3). Participants were told that we had already interviewed HCPs about what helped or hindered effective symptom management and now we wanted to understand their perspective too.

Data Collection

Interview topic guides (Supplementary information 4 and 5) were used. Interviews were conducted by EC over the telephone and audio recorded onto an encrypted Dictaphone. Files were downloaded onto a secure University of Leeds drive and then deleted from the Dictaphone. No video was recorded. The researcher made brief field notes directly after the interview on impression, emotion and participant backgrounds that were disclosed.

An Excel spreadsheet was used to facilitate data management. We explored the constructs of patient and carer experience as defined by our existing model. An inductive framework analysis was used to align data with themes in the existing conceptual model. A deductive approach was also used to identify new themes not included in the original model. Two researchers (EC and CP) independently conducted framework analysis on all transcripts. Data was then compared and discussed until a consensus data set was developed. The study is reported in accordance with Standards for Reporting Qualitative Research (SRQR) recommendations [ 11 ].

Twelve participants were interviewed in their own homes by telephone. In five interviews a family member or friend was also present, and they were interviewed as a dyad. One interview was with a carer of a patient (patient not interviewed) and one interview was with a patient alone. Interviews lasted between 21 and 45 min. Basic self-declared demographic information was collected (Table 2 ).

One person was approached by a research nurse and provided with participant information sheet. However, when they spoke with the researcher on the telephone it was clear that they had not read the participant information sheet. The individual declined for the information to be read out loud with them. Informed consent could therefore not be given and an interview was not carried out. Upon reflection, this person was keen to informally chat to the researcher but was perhaps seeking social interaction rather than research participation. All other participants completed the interview as planned.

Participant background was relevant as one carer and one patient, had experience of working in healthcare and this may have shaped their experience and understanding. Analysis was based on the framework method; transcription, familiarisation, coding, applying analytical framework (conceptual model), charting, interpretation.

Data aligned with 18 of 23 constructs in the professional based model (Table 3 ). Pseudonyms are used to protect confidentiality.

Four constructs that had featured in the healthcare professional based model did not feature in the patient and carer derived data. These were perhaps not unexpectedly related to characteristics of staff; Experience (of staff), Training (of staff), Psychological support (for staff) and the provision of formal psychological support (for patients). One construct ‘Guidelines and Evidence’ was not explicitly mentioned by patients and carers. However, a carer did comment that at time of referral to the hospice, the patient had been on two different does of co-codamol simultaneously ‘ You were on co-codamol, the 500/8 plus co-codamol 500/30’ (Patricia, carer) which suggested to the researchers that the patient had been taking the medication in a way contrary to guidelines. Medications were then optimised by hospice staff. Four additional patient and carer-derived themes were identified: Carer Burden, Communication, Medicines management and Impact of COVID-19 (Fig. 1 ).

The conceptual model of effective symptom management in palliative care was updated to also reflect patient and carer perspective. Specifically, the need for support with communication and medicines management plus consideration of the carer burden were included

Carer burden

Our HCP-based conceptual model identified a role for the carer in shaping symptom management experience in either a positive or negative way [ 1 ]. The patient and carer derived data presented here provides additional insight into their role and the activities required of them. Carer burden is a multifaceted experience, however our interview schedule specifically asked about symptom management experience.

The carer was sometimes responsible for raising concerns and initiating the referral for specialist palliative cares support ‘it was at some stage earlier in this year when I was a little anxious about your health and contacted the chemo wing at (hospital) and one of the nurses there thought it would be helpful to me and Patient to put us in touch with (the hospice) (Kathleen, carer).

Carers were enmeshed into the disease and symptom experience of the patient, referring to ‘we’ when talking about the patient’s cancer treatment, pain and referral to hospice.

Olivia (carer): Immune therapy we’d had a reaction to and we’d resolved the reaction but it concluded in stopping any treatment and we then went to a situation where we were not able to manage the pain from the cancer successfully and it was recommended by our oncologist that (the hospice) may have some expertise that we could….

Olivia (carer): Tap into…as I say that was a difficult decision for us to agree for Anthony to go into (the hospice).

However, on occasion the insight from the carer was not acted upon leading to a delay in support for distressing symptoms ‘ I kept saying to people, he’s losing weight, he’s in pain and they just kept saying well he shouldn’t be in this amount of pain ‘cos of what his bloods are like. And I kept saying well what you’re saying he should be like, I can tell you he’s not like and we’re not ones to you know erm (he) isn’t one to be bothering the doctor.’ (Sandra, carer).

Once the patient was receiving palliative care the carer took responsibility for obtaining and retaining knowledge either because the patient could not, due to memory problems from medication, or their condition, or they were not willing to do this for themselves.

Martin (patient): ‘she knows better than me ‘cos I’m always, I’m not very good at remembering stuff’

Martin (patient): I’m not interested no I understand you do have a very important role and she’s taken the lead on it now, that’s definitely the case’

And with another couple

Terry (patient): Sorry I’ve got my wife at the side of me ‘cos she knows better than me ‘cos I’m always, I’m not very good at remembering stuff.

Stacey (carer): I’m usually present yeah, I’m usually around. I tend to be the one that asks more questions.

However, in our interviews occasionally discordance between patient and carer opinion was seen with the carer rating the symptoms more troublesome than the patient’s recollection.

Interviewer: So was it (the pain) stopping you doing any activities that you had been able to do?

Marti, (patient): Oh I see, not particularly no

Mary (carer): I would probably disagree with that sorry. I would say that Martin’s management of the pain and our management of the pain and everything was kind of a constant thing, that’s all we, you know if felt like we were talking about it all the time, his pain’.

Despite an integral role in facilitating effective symptom management carers could feel unacknowledged, specifically by hospital staff. ‘ at the same time they’re telling me I’m not a carer and yet you know Wendy would be in a very sorry state if I wasn’t on the ball all the time’ (Patricia, carer). Specialist palliative care staff were better at providing acknowledgement and consideration of individual capabilities.

Patricia (carer): ‘So they understand that I’m not sort of hale and hearty and I’ve got my limitations….and it’s just lovely them knowing and actually accepting that I am caring for patient, we are doing the best that we can and that they are there for us.’. This simple step of acknowledgement was appreciated and a factor in allowing the carer to continue to support the patient.

Olivia (carer): ‘You know I do feel that it’s about me as well, it’s not just about Anthony which, it is really all about Anthony but you know it’s important that I continue with my wellbeing in order that I can support and look after him’ .

Communication

The impact of communication of effective symptom management occurred at different levels. As would be expected, communication needed to be tailored to the background, previous experience and outlook of the individual. In particular, we noted that a patient who had a healthcare background themselves welcomed more in-depth discussion and input into decision making.

Andrew (patient): I’ve dealt with people with cancers and terminal illnesses. Yeah, I know about syringe drives and everything…The important thing is to be able to discuss it and with my knowledge of medication as well, I mean I can discuss it in depth.’ .

Interestingly, this person also equated being admitted to the hospice with the use of a syringe driver and end of life, illustrating that regardless of the patient’s professional background, a thorough explanation without any assumptions on understanding would still be necessary. Andrew (patient):  ‘I mean I could go into (the hospice) at any time knowing this but with my work record and everything else, I know what it all entails I mean I’d probably go in and they’d probably want to put me on a syringe drive with Oramorph and Midazolam and Betamethasone and everything else and I know that is the beginning of the end once you start on the syringe driver and everything because it just puts you to sleep and just makes you comfortable and you don’t really have no quality of life’ .

Patients and carers valued being able to get in contact with someone when difficulties arose. Kathleen (carer): ‘Ease of communication is important to us so it’s easy to get in touch with somebody’ .

For some people, at the earlier stages after referral to the palliative care team, the only support that they required was just telephone contact.

Kathleen (carer): ‘What we have at the moment is a phone number to call and another lady, a nurse who actually rings us probably about once a fortnight yeah to check if we have any anxieties, problems.’ .

Palliative care professionals had a key role in mediating communication between patients and carers and other services. Kathleen (carer):  ‘she said yes, do you think Harry would mind us contacting the GP you know and I said I’m sure he would, if I think it’s a good idea he’d go along with it so that’s what we did, she did, she contacted our GP which meant that we got a telephone appointment and something happened very quickly’ .

This extended to explaining the purpose and results of tests such as X-rays.

Stacey (carer): Yeah he went when he was admitted he went for an Xray and that was the hospice, it was (clinical nurse specialist) that had organised that. We didn’t really know what was happening in the hospital but we came home again and he didn’t really know why he’d had the Xray or anything.

So when he spoke to the nurse at (the hospice), she sort of went through it all with him and talked him through it and that was really informative and helpful

There was a feeling that communication was better in specialist palliative care compared to the general National Health Service (NHS).

Olivia (carer): ‘There is an awful lot to be learned from the NHS about liaising and communications they could learn an awful lot from the way that the palliative care is operating and running’.

The carer also became an advocate for the patient’s needs and relaying information about symptoms and concerns to the healthcare professionals which the patient may not have themselves. Andrew (patient): ‘ I mean she (partner) tells (hospice nurse) things that I don’t’ cos‘ I mean I sometimes bottle quite a few things up and don’t say nothing but (partner) notices these things and then she will tell (hospice nurse) about them’.

This was also seen during a research interview, where the patient was willing for the carer to ‘tell the story’ on their behalf.

Mary (carer): Sorry I’m doing all the talking.

Martin (patient): Well no you need to because I’m useless.

We identified that patients had unmet needs in communicating about their condition ‘ Yeah, erm, again it’s, people are very reticent to use the word cancer. So they balk at saying the word’ (Wendy, patient)  and symptom experience with family and friends other than their regular carer.

Wendy (patient): I don’t know where she’s (my sister) at in terms of knowing about my symptoms and about the treatment I’m having, well no I do tell her actually, it’s not that I don’t but she has very bad arthritis…so I don’t push that too much because I’m thinking she’s actually in as much pain as I might be.’

This lack of communication could come from a position of wishing to protect the feelings of family members:

Wendy (patient): ‘Oh it’s been very difficult with family. You don’t know how much you want to tell them and you don’t know how far down the line you are anyway. I think over the years, I’ve been protecting my family’ )

Sometimes there were other important conversations that had not been held with family members.

Martin (patient): ‘I suppose my point in bringing up was because they’re particularly good kids and they are particularly, although I wouldn’t like them to hear me say it but they are, very good’ .

The work of medicines management

Medicines management was a time consuming and complex task, even for carers who has a background working in healthcare.

Sandra (carer): ‘I’m having to ring back my fourth phone call today to see is it a week off or have they forgotten to give him it. The communication isn’t great and I kind of think you know I’m kind of used to the NHS I’m, I know to ring and that sort of thing but I do think, I think if someone isn’t, got a health background or that sort of background there’s a lot of left to guesswork’ .

Commonly, the responsibility of managing the medicines could be delegated to the carer due to the side effects of the medication on the patient’s memory. It was felt that the patient would not have been able to manage by themselves. Mary (carer): ‘ a lot of the medication has made him not so aware, maybe a little bit muddled at times and his memory’s not as good as it was….you know he does forget quite easily so I wouldn’t, I have to say I wouldn’t trust him with his medication at all.’.

Carers took responsibility for ensuring medications were taken on time. As previously reported, this carer viewed this a joint endeavour with the patient.

Patricia (carer): I wake (patient) at 9 o’clock and make sure that she has her Lansoprazole and that she has her 12 hourly Longtech tablet. I generally am doing everything and as I say, we put the injection in at lunchtime every day and at night I remind her, not that she doesn’t, she doesn’t really need reminding but at 9 o’clock, I say have you had your tablets?’ .

The carer (who did not have a healthcare background) had developed an understanding of complex concepts such as the different modes of metabolism of medication for pain.

Patricia (carer): ‘So she’s now on a different set of pain relief which, the morphine was better but not better for her. So the pain killing stuff that she’s on is processed through the liver rather than through the kidneys and the kidney function has stabilised.’ .

Impact of COVID-19

Interviewees were asked about whether COVID-19 had impacted upon their experience. It seemed that for this selected group of patients and carers the impact was minimal.

Patricia (carer): ‘Can I just add that Covid seems to have, people have been complaining that this has stopped and that’s stopped whereas with Wendy her appointments, they’ve always wanted face to face and we’ve done phone appointments when it’s been appropriate and the care has been absolutely marvelous’.

Availably of hospice staff sometimes filled the gap in other services.

Kathleen (carer): ‘Because of lockdown and the virus and everything obviously all that (GP support) changed and you did start to feel a bit isolated and alone ‘cos you don’t always want to have to get in the car and drive to (hospital) for something if it’s not absolutely necessary and so therefore having someone else to talk to who knew more about things because obviously we’re learning as we go along Harry and I, it was very helpful’.

Problems were attributed to the general NHS system rather than being COVID-19 specific.

Sandra (carer): ‘I think as far as forthcoming information, I don’t think Covid has any bearing on that to be honest. You know, it just, I think it’s just an age-old problem in the NHS is communication.’ .

The close alignment of this patient and carer data with our HCP-based conceptual model provides additional reinforcement of the importance of multidisciplinary working and continuity of care in shaping symptom management experience. Indeed, the ability to see preferred member of general practices staff was recently reported as a factor associated with satisfaction with ends of life care in England [ 3 ].

Palliative care takes a holistic view of the patient and carer, the concerns of both being intertwined and interdependent. The observation that carers and patients viewed themselves as a single unit and talked about ‘we’ when describing the experience of symptoms and service referral, aligns with the dimension of the carer ‘living in the patients world’ and living in ‘symbiosis’ recently described by Borelli et al [ 4 ] and in earlier qualitative work with advanced cancer patients [ 5 ]. Carer opinion can be a close but not always perfect proxy of patient voice, even in this small sample we observed some discordance between patient and carer perception of symptom burden. However, carers were vitally important for communication with healthcare providers, relaying concerns, managing medication and generally advocating for the patient when they were unable or willing to do so. In the UK in 2022, the number of people living alone was 8.3 million. Since 2020, the number of people over 65 years old living alone has also increased [ 6 ]. Household composition is not a general indicator of wider social support networks, but these data do suggest that there could be a considerable number of people with palliative care needs without live-in carer support. This raises the questions of whether the experience of those living without a supportive carer can be equitable and how services might better facilitate this.

Home-based palliative care is thought to reduce symptom burden for patients with cancer [ 7 ]. To enable this, it is therefore vital that carers are adequately supported. Carer burden is a multifaceted experience, however our interview schedule specifically asked about symptom management experience. In agreement with the term ‘role strain’ in the review by Choi and Seo [ 8 ] we saw carers involvement in symptom management and in mediating communication between the patient and healthcare providers. Additional aspects reported by Choi et Seo include physical symptoms of the carer, psychological distress, impaired social relationships, spiritual distress, financial crisis, disruption of daily life and uncertainty [ 8 ] and these will not have all been probed by our interview topic guide.

Although in our original study HCPs talked about medicines from their perspective, the role of the carer was not discussed. Medicines management was an important way that carers facilitated effective symptom management but is a complex task. One carer commented: ‘I have to say that would be a nightmare if I wasn’t a nurse by background’ . Our data on the difficulties with medicine management are not novel and closely mirror the report of Pollock et al., [ 9 ]. Our findings echo and support their conclusions that managing medicine at home during end-of-life care could be improved by reducing the work of medicines management and improving co-ordination and communication in health care and we echo their calls for further research in the area.

We identified that patients and carers viewed mediating communication as an important role for healthcare professionals. This could be enabling communication between patients and carers and other healthcare professionals, for example arranging follow-up care or explaining information received. There was also a need for better communication between patients and their family members. As reviewed and synthesised by Murray et al., (2014) the importance of effective communication in palliative care has been long recognised [ 10 ]. In our study, an opportunity for HCPs to facilitate better communication about symptom experience between patients and their wider family was identified. Our previous survey of English hospices found that healthcare professionals, particularly nurses and allied health professionals felt that they needed more training in basic and advanced communication skills [ 11 ]. Having relevant experience and if the appropriate training was provided, staff may be well placed to support patients with developing an approach to these potentially difficult conversations. Participants were offered a choice of joint or individual interviews, but most chose to be interviewed as a dyad. It is possible that being interviewed as a pair may have altered the information disclosed. Although the aim was to discuss factors that impacted upon effective symptom management, discussions at times deviated to a more general appraisal of a participant’s experiences and all data collected may not be relevant to the research question.

When data was collected that lead to the development of the HCP-based model of effective symptom management (May to November 2019) a global pandemic was unforeseen. At the time of the patient and carer interview described here (October to December 2020), COVID-19 restrictions were in place in the UK. The patients and carers we interviewed were already receiving specialist palliative care support as outpatients. For these individuals it appeared that the impact of COVID-19 pandemic had had minimal impact on their care. The availability and reassurance of telephone support from hospice staff seemed in part to ameliorate the reduced support available from other services such as GPs. This contrasts sharply with the negative impact of COVID-19 on the experience of patients and carers in the more immediate end of life phase [ 12 ], receiving oncology care [ 13 ] or with cancer more generally [ 14 ]. Selection bias is likely as patients and carers with the capacity and willingness to participate in our research study possibly reflect those where the illness is in a more stable phase and immediate needs were being met. Indeed, participants talked about difficulties before referral to specialist palliative care and with other services but were overwhelmingly positive about the support currently being provided by the hospice.

Limitations

Due to the constraints of conducting a research study during the COVID-19 lockdown, more purposive sampling was not possible, this led to a lack of diversity in our sample. All participants identified themselves as of white British or white Scottish ethnicity which potentially means issues related to diverse ethnicities were not captured. All the patients who participated (and the non-participating patient whose carer was interviewed) lived with another person and had carer/family support. The experience of those managing their symptoms in isolation was therefore not captured. All participants were currently accessing support from a single hospice, the experience of those not yet receiving specialist support or receiving support from a different organisation may differ. The sample were diverse in age and included males and females, but all carers were female. Demographic information was not collected on socioeconomic background. COVID-19 restrictions necessitated the use of telephone interviews which may have lost subtle communications cues such as body language or conversely may have facilitated candid description. The transcripts do suggest that participants felt comfortable to tell their experience and they mostly spoke freely with limited prompting. One participant mentioned that he found it very difficult to leave the house, and therefore a telephone interview might have facilitated his inclusion. In some interviews more data was derived from the opinion of the carer than the patient, with the pair agreeing that the carer took responsibility for many tasks involved in managing the condition. We cannot be certain that carer interpretation accurately matches patient experience for all symptoms [ 15 ].

We set out to answer the question; does a healthcare professional data derived model represent the experience of patients and carers of people with advanced cancer? Overall, the answer was yes, as our healthcare professional based conceptual model of effective symptom management aligned well with the experience of patients with advanced cancer and their carers. Domains that did not align were those specifically related to professionals; experience (of staff), training (of staff), guidelines and evidence, psychological support (for staff) and the provision of formal psychological support (for patients), a resource patients and carers might be unaware of. Additional domains of carer burden, communication, medicine management and the impact of COVID-19 were identified. We make four recommendations arising from this research.

Routine appraisal and acknowledgement of carer burden, medicine management tasks and previous experience in healthcare roles.

Increased access to communication skills training for staff caring for palliative care patients and their families.

Review of patient communication needs with support provided where needed.

Further research into the symptom management experience of those living alone and exploration of how these people can be better supported.

Availability of data and materials

Original recordings generated and analysed during the current study are not publicly available due to protection of confidentiality. Anonymised transcripts with identifiable information removed may be available from the corresponding author on reasonable request.

Abbreviations

Coronavirus disease 2019

Healthcare professional

National Health Service

United Kingdom

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Acknowledgements

We are grateful to the patients and carers who in giving valuable time to share their experiences, made this research possible. We thank research nurses Kath Black and Angela Wray for their support with recruitment.

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by Yorkshire Cancer Research programme grant L412, RESOLVE: “Improving health status and symptom experience for people living with advanced cancer”. The sponsor had no role in study design or the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

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Original idea, EC and SP; Data collection, EC; Data Analysis, EC and CP; Data interpretation, All, Methodological oversight, SP and LZ; writing the manuscript, All. All authors contributed to the development of the updated conceptual model and approved the final submission.

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Chapman, E.J., Paley, C.A., Pini, S. et al. Updating a conceptual model of effective symptom management in palliative care to include patient and carer perspective: a qualitative study. BMC Palliat Care 23 , 208 (2024). https://doi.org/10.1186/s12904-024-01544-x

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Received : 08 March 2024

Accepted : 08 August 2024

Published : 19 August 2024

DOI : https://doi.org/10.1186/s12904-024-01544-x

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