meaning of qualitative data in research

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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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What is qualitative data? How to understand, collect, and analyze it

A comprehensive guide to qualitative data, how it differs from quantitative data, and why it's a valuable tool for solving problems.

What is qualitative research?

Importance of qualitative data.

• Differences between qualitative and quantitative data

Characteristics of qualitative data

Types of qualitative data.

• Pros and cons
• Collection methods
• Return to top

Everything that’s done digitally—from surfing the web to conducting a transaction—creates a data trail. And data analysts are constantly exploring and examining that trail, trying to find out ways to use data to make better decisions.

Different types of data define more and more of our interactions online—one of the most common and well-known being qualitative data or data that can be expressed in descriptions and feelings. 

This guide takes a deep look at what qualitative data is, what it can be used for, how it’s collected, and how it’s important to you. 

Key takeaways: 

Qualitative data gives insights into people's thoughts and feelings through detailed descriptions from interviews, observations, and visual materials.

The three main types of qualitative data are binary, nominal, and ordinal.

There are many different types of qualitative data, like data in research, work, and statistics. 

Both qualitative and quantitative research are conducted through surveys and interviews, among other methods. 

What is qualitative data?

Qualitative data is descriptive information that captures observable qualities and characteristics not quantifiable by numbers. It is collected from interviews, focus groups, observations, and documents offering insights into experiences, perceptions, and behaviors.

Qualitative data analysis cannot be counted or measured because it describes the data. It refers to the words or labels used to describe certain characteristics or traits.

This type of data answers the "why" or "how" behind the analysis . It’s often used to conduct open-ended studies, allowing those partaking to show their true feelings and actions without direction.

Think of qualitative data as the type of data you’d get if you were to ask someone why they did something—what was their reasoning? 

Qualitative research not only helps to collect data, it also gives the researcher a chance to understand the trends and meanings of natural actions. 

This type of data research focuses on the qualities of users—the actions behind the numbers. Qualitative research is the descriptive and subjective research that helps bring context to quantitative data. 

It’s flexible and iterative. For example: 

The music had a light tone that filled the kitchen.

Every blue button had white lettering, while the red buttons had yellow. 

The little girl had red hair with a white hat.

Qualitative data is important in determining the frequency of traits or characteristics. 

Understanding your data can help you understand your customers, users, or visitors better. And, when you understand your audience better, you can make them happier.  First-party data , which is collected directly from your own audience, is especially valuable as it provides the most accurate and relevant insights for your specific needs.

Qualitative data helps the market researcher answer questions like what issues or problems they are facing, what motivates them, and what improvements can be made.

Examples of qualitative data

You’ve most likely used qualitative data today. This type of data is found in your everyday work and in statistics all over the web. Here are some examples of qualitative data in descriptions, research, work, and statistics. 

Qualitative data in descriptions

Analysis of qualitative data requires descriptive context in order to support its theories and hypothesis. Here are some core examples of descriptive qualitative data:

The extremely short woman has curly hair and brilliant blue eyes.

A bright white light pierced the small dark space. 

The plump fish jumped out of crystal-clear waters. 

The fluffy brown dog jumped over the tall white fence. 

A soft cloud floated by an otherwise bright blue sky.

Qualitative data in research

Qualitative data research methods allow analysts to use contextual information to create theories and models. These open- and closed-ended questions can be helpful to understand the reasoning behind motivations, frustrations, and actions —in any type of case. 

Some examples of qualitative data collection in research:

What country do you work in? 

What is your most recent job title? 

How do you rank in the search engines? 

How do you rate your purchase: good, bad, or exceptional?

Qualitative data at work

Professionals in various industries use qualitative observations in their work and research. Examples of this type of data in the workforce include:

A manager gives an employee constructive criticism on their skills. "Your efforts are solid and you understand the product knowledge well, just have patience."

A judge shares the verdict with the courtroom. "The man was found not guilty and is free to go."

A sales associate collects feedback from customers. "The customer said the check-out button did not work.”

A teacher gives feedback to their student. "I gave you an A on this project because of your dedication and commitment to the cause."

A digital marketer watches a session replay to get an understand of how users use their platform.

Qualitative data in statistics

Qualitative data can provide important statistics about any industry, any group of users, and any products. Here are some examples of qualitative data set collections in statistics:

The age, weight, and height of a group of body types to determine clothing size charts. 

The origin, gender, and location for a census reading.

The name, title, and profession of people attending a conference to aid in follow-up emails.

Difference between qualitative and quantitative data

Qualitative and quantitative data are much different, but bring equal value to any data analysis. When it comes to understanding data research, there are different analysis methods, collection types and uses. 

Here are the differences between qualitative and quantitative data :

Qualitative data is individualized, descriptive, and relating to emotions.

Quantitative data is countable, measurable and relating to numbers.

Qualitative data helps us understand why, or how something occurred behind certain behaviors .

Quantitative data helps us understand how many, how much, or how often something occurred. 

Qualitative data is subjective and personalized.

Quantitative data is fixed and ubiquitous.

Qualitative research methods are conducted through observations or in-depth interviews.

Quantitative research methods are conducted through surveys and factual measuring. 

Qualitative data is analyzed by grouping the data into classifications and topics. 

Quantitative data is analyzed using statistical analysis.

Both provide a ton of value for any data collection and are key to truly understanding trending use cases and patterns in behavior . Dig deeper into quantitative data examples .

The characteristics of qualitative data are vast. There are a few traits that stand out amongst other data that should be understood for successful data analysis. 

Descriptive : describing or classifying in an objective and nonjudgmental way.

Detailed : to give an account in words with full particulars.

Open-ended : having no determined limit or boundary.

Non-numerical : not containing numbers. 

Subjective : based on or influenced by personal feelings, tastes, or opinions.

With qualitative data samples, these traits can help you understand the meaning behind the equation—or for lack of a better term, what’s behind the results. 

As we narrow down the importance of qualitative data, you should understand that there are different data types. Data analysts often categorize qualitative data into three types:

1. Binary data

Binary data is numerically represented by a combination of zeros and ones. Binary data is the only category of data that can be directly understood and executed by a computer.

Data analysts use binary data to create statistical models that predict how often the study subject is likely to be positive or negative, up or down, right or wrong—based on a zero scale.

2. Nominal data

Nominal data , also referred to as “named, labeled data” or “nominal scaled data,” is any type of data used to label something without giving it a numerical value. 

Data analysts use nominal data to determine statistically significant differences between sets of qualitative data. 

For example, a multiple-choice test to profile participants’ skills in a study.

3. Ordinal data

Ordinal data is qualitative data categorized in a particular order or on a ranging scale. When researchers use ordinal data, the order of the qualitative information matters more than the difference between each category. Data analysts might use ordinal data when creating charts, while researchers might use it to classify groups, such as age, gender, or class.

For example, a Net Promoter Score ( NPS ) survey has results that are on a 0-10 satisfaction scale. 

When should you use qualitative research?

One of the important things to learn about qualitative data is when to use it. 

Qualitative data is used when you need to determine the particular trends of traits or characteristics or to form parameters for larger data sets to be observed. Qualitative data provides the means by which analysts can quantify the world around them.

You would use qualitative data to help answer questions like who your customers are, what issues or problems they’re facing, and where they need to focus their attention, so you can better solve those issues.

Qualitative data is widely used to understand language consumers speak—so apply it where necessary. 

Pros and cons of qualitative data

Qualitative data is a detailed, deep understanding of a topic through observing and interviewing a sample of people. There are both benefits and drawbacks to this type of data. 

Pros of qualitative data

Qualitative research is affordable and requires a small sample size.

Qualitative data provides a predictive element and provides specific insight into development.

Qualitative research focuses on the details of personal choice and uses these individual choices as workable data.

Qualitative research works to remove bias from its collected data by using an open-ended response process.

Qualitative data research provides useful content in any thematic analysis.

Cons of qualitative data 

Qualitative data can be time-consuming to collect and can be difficult to scale out to a larger population.

Qualitative research creates subjective information points.

Qualitative research can involve significant levels of repetition and is often difficult to replicate.

Qualitative research relies on the knowledge of the researchers.

Qualitative research does not offer statistical analysis, for that, you have to turn to quantitative data.

Qualitative data collection methods

Here are the main approaches and collection methods of qualitative studies and data: 

1. Interviews

Personal interviews are one of the most commonly used deductive data collection methods for qualitative research, because of its personal approach.

The interview may be informal and unstructured and is often conversational in nature. The interviewer or the researcher collects data directly from the interviewee one-to-one. Mostly the open-ended questions are asked spontaneously, with the interviewer allowing the flow of the interview to dictate the questions and answers.

The point of the interview is to obtain how the interviewee feels about the subject. 

2. Focus groups

Focus groups are held in a discussion-style setting with 6 to 10 people. The moderator is assigned to monitor and dictate the discussion based on focus questions.

Depending on the qualitative data that is needed, the members of the group may have something in common. For example, a researcher conducting a study on dog sled runners understands dogs, sleds, and snow and would have sufficient knowledge of the subject matter.

3. Data records 

Data doesn’t start with your collection, it has most likely been obtained in the past. 

Using already existing reliable data and similar sources of information as the data source is a surefire way to obtain qualitative research. Much like going to a library, you can review books and other reference material to collect relevant data that can be used in the research.

For example, if you were to study the trends of dictionaries, you would want to know the past history of every dictionary made, starting with the very first one. 

4. Observation

Observation is a longstanding qualitative data collection method, where the researcher simply observes behaviors in a participant's natural setting. They keep a keen eye on the participants and take down transcript notes to find out innate responses and reactions without prompting. 

Typically observation is an inductive approach, which is used when a researcher has very little or no idea of the research phenomenon. 

Other documentation methods, such as video recordings, audio recordings, and photo imagery, may be used to obtain qualitative data.

Further reading: Site observations through heatmaps

5. Case studies

Case studies are an intensive analysis of an individual person or community with a stress on developmental factors in relation to the environment. 

In this method, data is gathered by an in-depth analysis and is used to understand both simple and complex subjects. The goal of a case study is to see how using a product or service has positively impacted the subject, showcasing a solution to a problem or the like. 

6. Longitudinal studies

A longitudinal study is where people who share a single characteristic are studied over a period of time. 

This data collection method is performed on the same subject repeatedly over an extended period. It is an observational research method that goes on for a few years and, in some cases, decades. The goal is to find correlations of subjects with common traits.

For example, medical researchers conduct longitudinal studies to ascertain the effects of a drug or the symptoms related.

Qualitative data analysis tools

And, as with anything—you aren’t able to be successful without the right tools. Here are a few qualitative data analysis tools to have in your toolbox: 

MAXQDA —A qualitative and mixed-method data analysis software 

Fullstory —A behavioral data and analysis platform

ATLAS.ti —A powerful qualitative data tool that offers AI-based functions 

Quirkos —Qualitative data analysis software for the simple learner

Dedoose —A project management and analysis tool for collaboration and teamwork

Taguette —A free, open-source, data analysis and organization platform 

MonkeyLearn —AI-powered, qualitative text analysis, and visualization tool 

Qualtrics —Experience management software

Frequently asked questions about qualitative data

Is qualitative data subjective.

Yes, categorical data or qualitative data is information that cannot generally be proven. For instance, the statement “the chair is too small” depends on what it is used for and by whom it is being used.

Who uses qualitative data?

If you’re interested in the following, you should use qualitative data:

Understand emotional connections to your brand

Identify obstacles in any funnel, for example with session replay

Uncover confusion about your messaging

Locate product feature gaps 

Improve usability of your website, app, or experience

Observe how people talk, think, and feel about your brand

Learn how an organization selects vendors and partners

What are the steps for qualitative data?

1. Transcribe your data : Once you’ve collected all the data, you need to transcribe it. The first step in analyzing your data is arranging it systematically. Arranging data means converting all the data into a text format. 

2. Organize your data : Go back to your research objectives and organize the data based on the questions asked. Arrange your research objective in a table, so it appears visually clear. Avoid working with unorganized data, there will be no conclusive results obtained.

3. Categorize and assign the data : The coding process of qualitative data means categorizing and assigning variables, properties, and patterns. Coding is an important step in qualitative data analysis, as you can derive theories from relevant research findings. You can then begin to gain in-depth insight into the data that help make informed decisions.

4. Validate your data : Data validation is a recurring step that should be followed throughout the research process. There are two sides to validating data: the accuracy and reliability of your research methods, which is the extent to which the methods produce accurate data consistently. 

5. Conclude the data analysis : Present your data in a report that shares the method used to conduct the research studies, the outcomes, and the projected hypothesis of your findings in any related areas.

Is qualitative data better than quantitative data?

One is not better than the other, rather they work cohesively to create a better overall data analysis experience. Understanding the importance of both qualitative and quantitative data is going to produce the best possible data content analysis outcome for any study. 

Further reading : Qualitative vs. quantitative data — what's the difference?

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• What is qualitative data?

Last updated

8 February 2023

Reviewed by

Miroslav Damyanov

If you're wondering how qualitative data can help your business or innovative research efforts, keep reading. This is the ultimate guide to understanding what it is, how it works, and where to begin in collecting and analyzing data.

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For those who want to leverage the power of qualitative data, it's essential to understand the basic definitions first. 

When working with qualitative data, you're collecting and reviewing metrics that characterize and approximate. You source this data through observation, including interviews, surveys , and focus groups .

With responses and analytics in hand, you can categorize your findings in terms of feelings, attributes, or properties.

• Qualitative vs. quantitative data

You've likely heard of quantitative data. It's equally important to understand the distinctions between quantitative and qualitative datasets. While qualitative data measures feelings, behaviors, and properties, quantitative data are measures of values expressed as numbers.

Quantitative data answers questions like  how much  or  how often . Qualitative data will answer questions like  what type  or  why . Because qualitative data evaluate deeper sentiments, they're best at illustrating thought processes and behaviors behind the raw quantitative data.

• Importance of qualitative data

Qualitative data plays a more pivotal role in research and business today, especially because it helps unravel the characteristics and behavioral motivations. This data allows researchers and managers to "qualify" the environment or ecosystem they're studying. These analytics allow you to dive deeper into understanding people’s emotional or perceptual motivations. 

These metrics provide insights about target audiences and customer decision-making preferences in business. These datasets are great for solving problems and paving the way for innovations. 

Properly collecting and analyzing qualitative data will provide all the insights you need to prioritize your focus, address challenges, and resolve issues.

• Types of qualitative data

Since qualitative data is more of an exploratory process, it involves a more in-depth look at behaviors and concepts. And there are various types of qualitative data to explore. 

How you collect qualitative data and the channels you use will help you organize your results. Most data fits into one of three pillar categories: Nominal, binary, and ordinal. But all initiatives should answer a  primary research question.

Most qualitative data methods aim to:

Gain behavioral insights

Understand reasoning

Explore motivations

Identify emotional connections

• Qualitative data examples

It may be helpful to see qualitative data examples to better understand how to apply these collection and analysis methods to your model. Here are a few simplified samples to illustrate the value of these metrics in business and research.

Measuring characteristics

If you're studying a group of women, you might dive deeper to measure their characteristics. For example, you can collect qualitative data about the various hair colors or current jobs of the women in your study group. These qualitative metrics are great for developing marketing personas in business applications.

Measuring behaviors

Imagine you're studying a group of children in a room full of different toys. Measuring qualitative data might include observing which toys those children choose to play with first. Studying these behaviors will help you better understand the behavior behind what attracts a child to a particular type of toy, which is great for product innovation initiatives.

Measuring motivations

Consumers buying products or services will make their purchasing decisions according to personal motivations. Qualitative data surveys can help you study particular groups to evaluate why those consumers decided to buy. Knowing if your target audience is more motivated by price point, free shipping, or customer service will help you change how you engage them.

• Qualitative data collection methods

When you're ready to explore collecting and analyzing qualitative data, there are several collection methods to consider. Surveys and focus groups are great tools, but there are other methods, too. Each method is uniquely beneficial to specific metrics and research goals.

One-on-one interviews

One of the most common data collection methods, this qualitative research effort provides a more personal approach to determining sentiments and behaviors. Interviews with open-ended questions net the most in-depth responses and results.

Focus groups

Usually limited to ten or fewer participants, this method assigns a moderator to initiate a group discussion of ideas and sentiments. Members may all have something in common, but their responses will contribute to your qualitative datasets.

Case studies

These methods are ideal for collecting specific in-depth data using a combination of qualitative data sources to gain contextual knowledge about a specific real-world phenomenon. 

Longitudinal studies

This research approach is where you collect data from the same participants repeatedly. It's an observational model for dynamically comparing a captured group's results over days or even decades.

Record keeping

With this method, you can use existing sources of information to inspire new research. Much like visiting a library, you can study reference material to discover new qualitative data you should be studying.

Observation

This method involves a researcher immersing into a setting to watch and take notes of participants. Documentation might be in the form of note-taking, video observation, photography, or audio recording. 

• What is qualitative data analysis?

Once you've collected the qualitative data, it's time to evaluate and analyze it to produce inferences and actionable applications. There are no hard or fast rules for interpreting the data you collect. However, there are two primary approaches to understanding the qualitative data you've assembled for review. 

Deductive approach

If you've already outlined a structure as part of your data collection process, you're using a deductive approach to analyzing data. You use this analysis method when you already have an idea about the responses in the dataset. This approach is often easier to execute since you establish much of the groundwork during the data collection stage.

Inductive approach

This method involves analyzing qualitative data when you have no idea what the responses, results, or research phenomenon will be. It's more time-consuming to study data this way, but it can also be where researchers find revolutionary anomalies and lightbulb, a-ha solutions.

• How do you begin analyzing qualitative data?

To help you begin analyzing your recently collected qualitative data, you can loosely follow these basic steps to initiate your analysis.

Arrange your data into systems or categories into a software platform or analysis tool that allows you to easily visualize what you've collected.

Organize your qualitative data according to your research objectives using tables, spreadsheets, or visually appealing graphics.

Assigning proper codes for your data will help you compress vast libraries of information. Coding really translates to categories but takes it a step further by assigning properties and patterns. Codes will help you draw conclusions later.

Start validating your qualitative data to identify viable collection samples and eliminate any flawed or misconstrued datasets. Verify the accuracy of the collection methods and confirm the reliability and accuracy of the data you’ve collected.

Conclude your data with systematic presentation in a condensed report. In this step, you'll outline the methods you used in collection, the researchers involved, and the approach. You'll share the positives, negatives, and limitations of your study. Here, you draw inferences about your findings and offer suggestions for action or future research.

• Sharing qualitative analysis

When you're ready to share your qualitative analysis findings, you can choose a variety of formats to suit your presentation audience. These might include:

Digital or physical reports

Images or infographics

Audio or visual materials

Scanned historical documents

Observation dictations

Field notes

When explaining your analysis, it's best to share the purpose and parameters of your study. Then you can offer immediate results. Your qualitative analysis process will allow you to draw conclusions, apply judgment, and determine the next steps based on your unique scenario. 

• Advantages of qualitative data

There are inherent advantages to applying qualitative data collection and analysis methods to business and research projects. These are the three most pivotal benefits of tapping into regular qualitative data initiatives ongoing:

Get in-depth data beyond the numbers.

Understand participants or consumer behaviors more intuitively.

Discover rich data you can use and reuse well into the future.

• Disadvantages of qualitative data

A few disadvantages of qualitative data practices are worth noting. Before adopting these approaches, consider these potential setbacks:

Proper qualitative data collection and analysis is time-consuming.

The data can be hard to generalize, especially with fewer participants.

The researcher’s data analysis skills and the results are directly correlated.

• Qualitative analysis tools

As complex as qualitative data analysis can be, you don't have to go it alone or recreate the wheel. Many incredible tools and resources are available to simplify each step of the research process. 

Explore software solutions that streamline how you collect data, like online survey tools or customer questionnaires. Discover the design and data management software solutions out there intended to help you categorize and organize your data. 

Software packages for qualitative data analysis are your best friend when entering the analysis stage and looking for patterns and conclusions.

Start leveraging the many benefits of qualitative data analysis for your business or research project. Remember to reference this guide as you develop your studies and determine your methods.

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

By Nick Jain

Published on: June 21, 2023

Table of Contents

What is Qualitative Research?

5 key types of qualitative research, examples of qualitative research, qualitative research methods: the top 4 techniques, qualitative research best practices.

Qualitative research is defined as an exploratory metho d that aims to understand complex phenomena, often within their natural settings, by examining subjective experiences, beliefs, attitudes, and behaviors.

Unlike quantitative research , which focuses on numerical measurements and statistical analysis, qualitative research employs a range of data collection methods to gather detailed, non-numerical data that can provide in-depth insights into the research topic.

Here are the key characteristics of Qualitative Research:

• Subjectivity : Qualitative research acknowledges the subjective nature of human experiences and perceptions. It recognizes that individuals interpret and construct meaning based on their unique perspectives, cultural backgrounds, and social contexts. Researchers using qualitative methods aim to capture this subjectivity by engaging in detailed qualitative observations , interviews, and analyses that capture the nuances and complexities of human behavior.
• Contextualization : Qualitative research places a strong emphasis on the context in which social phenomena occur. It seeks to understand the interconnectedness between individuals, their environments, and the broader social structures that shape their experiences. Researchers delve into the specific settings and circumstances that influence the behavior and attitudes of participants, aiming to unravel the intricate relationships between different variables.
• Flexibility : Qualitative research is characterized by its flexibility and adaptability. Researchers have the freedom to modify their research design and methods during the course of the study based on emerging insights and new directions. This flexibility allows for iterative and exploratory research, enabling researchers to delve deeper into the subject matter and capture unexpected findings.
• Interpretation and meaning-making : Qualitative research recognizes that meaning is not fixed but constructed through social interactions and interpretations. Researchers engage in a process of interpretation and meaning-making to make sense of the data collected. This interpretive approach allows researchers to explore multiple perspectives, cultural influences, and social constructions that shape participants’ experiences and behaviors.
• Richness and depth : One of the key strengths of qualitative research is its ability to generate rich and in-depth data. Through methods such as interviews, focus groups , and participant observation, researchers can gather detailed narratives and descriptions that go beyond surface-level information. This depth of data enables a comprehensive understanding of the research topic, including the underlying motivations, emotions, and social dynamics at play.
• Inductive reasoning : Qualitative research often employs an inductive reasoning approach. Instead of starting with preconceived hypotheses or theories, researchers allow patterns and themes to emerge from the data. They engage in iterative cycles of data collection and analysis to develop theories or conceptual frameworks grounded in the empirical evidence gathered. This inductive process allows for new insights and discoveries that may challenge existing theories or offer alternative explanations.
• Naturalistic setting : Qualitative research frequently takes place in naturalistic settings, where participants are observed and studied in their everyday environments. This setting enhances the ecological validity of the research, as it allows researchers to capture authentic behaviors, interactions, and experiences. By observing individuals in their natural contexts, researchers can gain a deeper understanding of how social phenomena unfold in real-world situations.

Learn more: What is Qualitative Observation?

Here are the 5 key qualitative research types that are employed in studies:

1. Phenomenology : This type of research focuses on understanding the essence and meaning of a particular phenomenon or experience as perceived by individuals who have lived through it. It seeks to capture the subjective experiences and perspectives of participants.

2. Ethnography : Ethnographic research involves immersing oneself in a specific cultural or social group to observe and understand its practices, customs, beliefs, and values. Researchers spend extended periods of time within the community to gain a holistic view of its way of life.

3. Grounded Theory: Grounded theory aims to generate new theories or conceptual frameworks based on the analysis of data collected from interviews, observations, or documents. It involves systematically coding and categorizing data to identify patterns and develop theoretical explanations.

4. Case Study : In a case study, researchers conduct an in-depth examination of a single individual, group, or event to gain a detailed understanding of the subject of study. This approach allows for rich contextual information and can be particularly useful in exploring complex and unique cases.

5. Narrative Research: Narrative research focuses on analyzing the stories and personal narratives of individuals to gain insights into their experiences, identities, and sense-making processes. It emphasizes the power of storytelling in constructing meaning.

Example 1. A researcher conducting a phenomenological study might explore the lived experiences of individuals who have survived a natural disaster to understand the psychological and emotional impact of such events.

Example 2. An ethnographer might immerse themselves in a remote indigenous community to study their cultural practices, rituals, and social dynamics.

Example 3. A grounded theory study might investigate the coping mechanisms employed by cancer patients by conducting interviews and analyzing their experiences.

Example 4. A case study could involve examining a specific company’s organizational culture to understand its impact on employee performance and job satisfaction.

Example 5. A narrative research project might analyze the personal narratives of individuals who have experienced significant life transitions, such as migration or career changes, to understand the underlying meaning-making processes.

Learn more: What is Qualitative Market Research?

Here are the best qualitative research methods that offer unique advantages in capturing rich data, facilitating in-depth analysis, and generating comprehensive findings:

1. In-Depth Interviews

One of the most widely used qualitative research techniques is in-depth interviews. This method involves conducting one-on-one interviews with participants to gather rich, detailed information about their experiences, perspectives, and opinions. In-depth interviews allow researchers to explore a participant’s thoughts, emotions, and motivations, providing deep insights into their behavior and decision-making processes. The flexibility of this method allows for the exploration of individual experiences in great detail, making it particularly suitable for sensitive topics or complex phenomena. Through careful probing and open-ended questioning, researchers can develop a comprehensive understanding of the participant’s worldview, uncovering hidden patterns, and generating new hypotheses.

2. Focus Groups

Focus group research involves the gathering of a small group of individuals (typically 6-10) who share common characteristics or experiences. This method encourages participants to engage in open discussions facilitated by a skilled moderator. Focus groups offer a dynamic environment that allows participants to interact, share their perspectives, and build upon each other’s ideas. This method is particularly useful for exploring group dynamics, collective opinions, and societal norms. By observing interactions within the group, researchers can gain valuable insights into how social influences shape individual attitudes and behaviors. Focus groups also allow for the exploration of diverse viewpoints, enabling researchers to identify patterns, contradictions, and shared experiences.

3. Observational Research

Observational research involves systematically observing and documenting participants’ behaviors and interactions within their natural environments. This method provides researchers with a direct window into real-life contexts, allowing for a comprehensive understanding of social interactions, cultural practices, and behavioral patterns. Whether conducted through participant observation or unobtrusive observation, this method eliminates the potential biases associated with self-reporting, as participants’ actions speak louder than words. Observational research is especially valuable in studying nonverbal communication, contextual factors, and complex social systems. It can also provide insights into unarticulated behaviors or experiences that may be difficult to capture through other methods. However, careful planning, ethical considerations, and the need for prolonged engagement are crucial for conducting successful observational research .

4. Case Studies

Case studies involve an in-depth examination of a specific individual, group, organization, or event. Researchers collect data through various sources, such as interviews, observations, documents, and artifacts, to construct a holistic understanding of the case under investigation. This method allows for an exploration of complex social phenomena in their real-life context, uncovering rich, detailed insights that may not be accessible through other methods. Case studies provide an opportunity to examine unique or rare cases, delve into historical contexts, and generate context-specific knowledge. The findings from case studies are often highly detailed and context-bound, offering rich descriptions and contributing to theory development or refinement.

Qualitative research methods offer a range of powerful tools for exploring subjective experiences, meanings, and interpretations. In-depth interviews allow for the exploration of individual perspectives, while focus groups illuminate group dynamics. Observational research provides a direct view of participants’ behaviors, and case studies offer a holistic understanding of specific cases. By leveraging these qualitative methods, researchers can unveil deep insights, capture complex phenomena, and generate context-specific knowledge.

• Clear Research Objectives: Clearly define the qualitative research objectives, questions, or hypotheses that guide the study. This helps maintain focus and ensures that data collection and analysis are aligned with the research goals.
• Sampling Strategy: Select participants or cases that are relevant to the qualitative research questions and provide diverse perspectives. Purposeful sampling techniques, such as maximum variation or snowball sampling, can help ensure the inclusion of a wide range of experiences and viewpoints.
• Data Collection Rigor: Employ rigorous qualitative data collection techniques to ensure the accuracy, credibility, and depth of the findings. This may involve conducting multiple interviews or qualitative observations , using multiple sources of data, and taking detailed field notes.
• Ethical Considerations: Adhere to ethical guidelines and obtain informed consent from participants. Protect the privacy, confidentiality, and anonymity of participants and ensure their voluntary participation throughout the qualitative research process.
• Data Analysis: Utilize systematic and rigorous approaches to analyze qualitative research data. This may involve coding, categorizing, and identifying patterns or themes within the data. Software tools like NVivo or ATLAS.ti can assist in organizing and analyzing large datasets.
• Triangulation: Enhance the validity and reliability of the findings by employing triangulation. Triangulation involves using multiple data sources, methods, or researchers to corroborate and validate the results, reducing the impact of researcher bias.
• Member Checking: Share the preliminary findings with participants to verify the accuracy and interpretation of their data. Member checking allows participants to provide feedback and corrections, enhancing the trustworthiness of the research.
• Reflexive Journaling: Maintain a reflexive journal throughout the research process to record reflections, insights, and decisions made during data collection and analysis. This journal can serve as a valuable tool for ensuring transparency and traceability in the research process.
• Clear and Transparent Reporting: Present the research findings in a clear, coherent, and transparent manner. Clearly describe the research methodology, data collection, and analysis processes. Provide rich and thick descriptions of the findings, supported by direct quotations and examples from the data.

By following these best practices, qualitative researchers can enhance the rigor, credibility, and trustworthiness of their research, leading to valuable and meaningful insights into the complex phenomena under investigation.

Learn more: What is Customer Experience (CX) Research?

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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What is qualitative research?

Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within their natural setting. It focuses on the "why" rather than the "what" of social phenomena and relies on the direct experiences of human beings as meaning-making agents in their every day lives. Rather than by logical and statistical procedures, qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

University of Utah College of Nursing, (n.d.). What is qualitative research? [Guide] Retrieved from  https://nursing.utah.edu/research/qualitative-research/what-is-qualitative-research.php#what 

The following video will explain the fundamentals of qualitative research.

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• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." [2] Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Qualitative Research Approaches

Ethnography

Ethnography as a research design originates in social and cultural anthropology and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc, through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded theory

Grounded Theory is the "generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior." [5] Unlike quantitative research, which is deductive and tests or verifies an existing theory, grounded theory research is inductive and, therefore, lends itself to research aimed at social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is the "study of the meaning of phenomena or the study of the particular.” [5] At first glance, it might seem that Grounded Theory and Phenomenology are pretty similar, but the differences can be seen upon careful examination. At its core, phenomenology looks to investigate experiences from the individual's perspective. [2] Phenomenology is essentially looking into the "lived experiences" of the participants and aims to examine how and why participants behaved a certain way from their perspective. Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources. In contrast, Phenomenology focuses on describing and explaining an event or phenomenon from the perspective of those who have experienced it.

Narrative research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called a "thick" or "rich" description and is a strength of qualitative research. Narrative research is rife with the possibilities of "thick" description as this approach weaves together a sequence of events, usually from just one or two individuals, hoping to create a cohesive story or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be "opportunities for innovation." [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. [4] It is valuable for qualitative and quantitative researchers to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontologies and epistemologies. Ontology is defined as the "assumptions about the nature of reality,” whereas epistemology is defined as the "assumptions about the nature of knowledge" that inform researchers' work. [2] It is essential to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a complete understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, researchers must understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist versus postpositivist

To further understand qualitative research, we must discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social and natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in their research. It stems from positivist ontology, that there is an objective reality that exists that is wholly independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained, but could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world,” and therefore, postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are also constructivist, meaning they think there is no objective external reality that exists but instead that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. "Constructivism contends that individuals' views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality.” [6]  constructivist thought focuses on how "reality" is not a fixed certainty and how experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike positivist views, that there is not necessarily an "objective"reality we all experience. This is the ‘relativist’ ontological view that reality and our world are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have. It can even change the role of the researchers. [2] For example, is the researcher an "objective" observer, such as in positivist quantitative work? Or is the researcher an active participant in the research, as in postpositivist qualitative work? Understanding the philosophical base of the study undertaken allows researchers to fully understand the implications of their work and their role within the research and reflect on their positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale for being the most informative.
• Criterion sampling selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques, including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic, and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant-observers to share the experiences of the subject or non-participants or detached observers.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed, which may then be coded manually or using computer-assisted qualitative data analysis software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results could also be in the form of themes and theory or model development.

Dissemination

The healthcare team can use two reporting standards to standardize and facilitate the dissemination of qualitative research outcomes. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research. [13]

Applications

Many times, a research question will start with qualitative research. The qualitative research will help generate the research hypothesis, which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data to better understand what the numbers truly mean and their implications. The qualitative techniques can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research, researchers can explore poorly studied subjects with quantitative methods. These include opinions, individual actions, and social science research.

An excellent qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure no omissions of part of the target population. A proper collection method should be selected that will help obtain the desired information without overly limiting the collected data because, often, the information sought is not well categorized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of why teens start to smoke and factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered "cool," and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current nonsmokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the survey results to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the primary factor that keeps teens from starting to smoke, and peer pressure was the primary factor that contributed to teens starting smoking. The researcher can go back to qualitative research methods to dive deeper into these for more information. The researcher wants to focus on keeping teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly in the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure to smoke. The researcher finds a local park where many local teenagers hang out and sees that the smokers tend to hang out in a shady, overgrown area of the park. The researcher notes that smoking teenagers buy their cigarettes from a local convenience store adjacent to the park, where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk populations their perceptions of the changes and what factors are still at play, and quantitative research that includes teen smoking rates in the community and the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or combined with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation not only to help generate hypotheses that can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are. Qualitative research allows researchers to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many ways, including the evaluation criteria. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. Credibility, transferability, dependability, and confirmability are the correlating concepts in qualitative research. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept on the left and the qualitative concept on the right:

• Internal validity: Credibility
• External validity: Transferability
• Reliability: Dependability
• Objectivity: Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid, qualitative researchers should ensure that their work has credibility. 

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple data collection methods to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable if we interviewed the magician, backstage hand, and the person who "vanished." In qualitative research, triangulation can include telephone surveys, in-person surveys, focus groups, and interviews and surveying an adequate cross-section of the target demographic.
• Peer examination: A peer can review results to ensure the data is consistent with the findings.

A "thick" or "rich" description can be used to evaluate the transferability of qualitative research, whereas an indicator such as an audit trail might help evaluate the dependability and confirmability.

• Thick or rich description:  This is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was conducted. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original information records should also be kept (eg, surveys, notes, recordings).

One issue of concern that qualitative researchers should consider is observation bias. Here are a few examples:

• Hawthorne effect: The effect is the change in participant behavior when they know they are being observed. Suppose a researcher wanted to identify factors that contribute to employee theft and tell the employees they will watch them to see what factors affect employee theft. In that case, one would suspect employee behavior would change when they know they are being protected.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens unconsciously for the participant, so it is essential to eliminate or limit the transmission of the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in contrived scenarios with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative or quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research is not an island apart from quantitative research but an integral part of research methods to understand the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is essential for all healthcare team members as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research. Much of the qualitative research data acquisition is completed by numerous team members, including social workers, scientists, nurses, etc. Within each area of the medical field, there is copious ongoing qualitative research, including physician-patient interactions, nursing-patient interactions, patient-environment interactions, healthcare team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Qualitative data are data representing information and concepts that are not represented by numbers. They are often gathered from interviews and focus groups, personal diaries and lab notebooks, maps, photographs, and other printed materials or observations. Qualitative data are distinguished from quantitative data , which focus primarily on data that can be represented with numbers. 

Qualitative data can be analyzed in multiple ways. One common method is data coding, which refers to the process of transforming the raw collected data into a set of meaningful categories that describe essential concepts of the data. Qualitative data and methods may be used more frequently in humanities or social science research and may be collected in descriptive studies.

Examples of qualitative data are the transcript of an interview and data collected in free text fields in a survey. 

There are many tools available for qualitative data analysis, depending on the data type. Some popular tools include:

• NVIVO: https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home/  
• Dedoose: https://www.dedoose.com/
• Taguette: https://www.taguette.org/

Relevant Literature

Deakin University Library created a great video explaining the difference between qualitative and quantitative research and data:

https://www.youtube.com/watch?v=4iws9XCyTEk

This guide provides a full look at qualitative data, including how and why it’s collected and used:

https://www.fullstory.com/qualitative-data/  

An Overview of Qualitative Research Methods

Direct Observation, Interviews, Participation, Immersion, Focus Groups

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

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

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

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

Key Takeaways

Methods of qualitative research include:

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

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

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

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

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

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

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

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

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

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

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

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

Pros and Cons

Qualitative research has both benefits and drawbacks.

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

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

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

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

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

• How to Conduct a Sociology Research Interview
• What Is Participant Observation Research?
• Immersion Definition: Cultural, Language, and Virtual
• Definition and Overview of Grounded Theory
• The Differences Between Indexes and Scales
• Pros and Cons of Secondary Data Analysis
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• The Different Types of Sampling Designs in Sociology
• Principal Components and Factor Analysis
• Sociology Explains Why Some People Cheat on Their Spouses
• Deductive Versus Inductive Reasoning
• How to Construct an Index for Research
• Data Sources For Sociological Research
• A Review of Software Tools for Quantitative Data Analysis
• Constructing a Deductive Theory
• Ethical Considerations in Sociological Research

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Definition for qualitative data analysis.

Home » Definition for Qualitative Data Analysis

Understanding qualitative data is crucial for identifying patterns in human behavior and opinions. By analyzing this type of data, researchers can uncover insights that numbers alone cannot provide. This exploration often involves carefully reviewing interview transcripts, surveys, and open-ended responses, allowing for a deeper understanding of participants' experiences and perspectives.

Pattern identification plays a key role in qualitative data analysis, as it helps to synthesize findings into meaningful themes. Researchers look for recurring ideas, emotions, and trends that emerge from the data. This process not only enriches the analysis but also enhances the overall reliability and depth of insights gathered, ultimately supporting informed decision-making.

What is Qualitative Data Analysis?

Qualitative data analysis is a method used to interpret and understand non-numerical data, such as text, audio, and video. This form of analysis focuses on exploring deeper meanings and patterns within the data rather than merely quantifying it. A key element of qualitative analysis is pattern identification, which involves recognizing recurring themes, concepts, or insights that emerge from the collected data. This process helps researchers and teams uncover significant relationships and understand underlying motivations or behaviors.

The value of qualitative analysis lies in its ability to provide rich context and insights that quantitative data may overlook. For example, when analyzing customer feedback, identifying patterns can reveal customer values or pain points. Overall, qualitative data analysis enhances the understanding of complex issues, allowing for more informed decision-making based on the nuanced information gathered. By focusing on qualitative data, researchers can ensure a comprehensive perspective that is essential for effective strategy development and improved outcomes.

Core Concepts and Techniques

Pattern identification is crucial in qualitative data analysis as it enables researchers to discern trends and recurring themes within their data. By carefully observing the nuances of participant responses, researchers can develop a deeper understanding of the underlying experiences and motivations that shape those perspectives. This process allows for richer insights that can inform decision-making and strategy development.

To effectively identify patterns, researchers can employ several techniques. First, thematic analysis involves coding data and categorizing themes to unveil commonalities. Second, narrative analysis focuses on understanding the context and structure of participants' stories. Third, constant comparative analysis juxtaposes new data against existing information to refine emerging patterns. Each of these techniques serves to enhance the richness of the qualitative data analysis, empowering researchers to uncover the meaningful connections that drive insights. Understanding these techniques equips researchers with the tools needed to extract valuable findings from their qualitative data.

Importance of Pattern Identification in Qualitative Data

Pattern identification plays a crucial role in qualitative data analysis, serving as a bridge to understanding complex insights. By carefully analyzing qualitative data, researchers can uncover recurring themes, trends, and sentiments expressed by participants. This process not only enhances the richness of the data but also helps the researchers connect findings to specific objectives or questions. Identifying these patterns allows for a more nuanced interpretation of qualitative data, highlighting deeper implications that may otherwise remain hidden.

Moreover, the significance of pattern identification extends beyond mere data analysis; it informs decision-making and strategic planning. When researchers recognize patterns, they can identify common needs and preferences among participants. This understanding can drive improvements in products, services, or overall experiences. Thus, mastering the art of pattern identification in qualitative data fosters a more informed and actionable approach to research outcomes, ultimately leading to enhanced engagement and satisfaction among stakeholders.

Steps Involved in Qualitative Data Analysis

Qualitative data analysis involves several essential steps that guide researchers in interpreting complex data. The first step is data collection, where researchers gather rich narrative information through interviews, focus groups, or open-ended surveys. Following this, data organization is crucial. By sorting and categorizing the gathered data, analysts begin to prepare for deeper exploration.

Next comes pattern identification, a vital aspect of qualitative analysis. Researchers look for recurring themes, similarities, and differences within the data. This step not only helps in recognizing significant insights but also improves understanding of the underlying motivations or experiences of participants. Lastly, researchers interpret the identified patterns, forming conclusions that address the original research questions. This cyclical process ultimately enhances the richness of insights drawn from qualitative research, providing a comprehensive view of the research subject.

Data Collection Methods and Identifying Patterns

When collecting qualitative data, various methods can be employed to ensure a rich understanding of the subject matter. These methods often include interviews, focus groups, and observations, each allowing researchers to gather nuanced insights. By engaging participants in open dialogue, researchers can uncover deeper meanings and emotions tied to their experiences. Accurate data collection is crucial as it forms the foundation for effective pattern identification later in the analysis process.

Once the data is collected, identifying patterns becomes essential. Analyzing responses from participants can reveal recurring themes, trends, and sentiments within the data. Techniques such as coding and categorization help researchers connect different responses to overarching patterns. Visualization tools can further assist in recognizing these connections, as they allow researchers to map insights clearly. This methodical approach not only clarifies complex information but also aids in formulating actionable strategies based on the gathered insights.

Coding and Thematic Analysis for Pattern Identification

Coding and thematic analysis serve as essential techniques in qualitative data analysis, particularly for pattern identification. By systematically coding data, researchers can break down complex information into manageable segments. This process helps highlight recurring themes and ideas, allowing for a deeper understanding of the data at hand. Thematic analysis facilitates the extraction of insights by focusing on significant themes that emerge through the coding process.

Moreover, effective coding requires a thorough examination of the data to ensure nuanced patterns are recognized. Identifying patterns then enables researchers to draw conclusions and make informed decisions based on the collective findings. By rigorously applying these techniques, the qualitative analysis transforms raw data into meaningful insights that can influence various fields. Ultimately, coding and thematic analysis enhance understanding, guiding future directions and strategies rooted in data-driven evidence.

Conclusion: The Vital Role of Pattern Identification in Qualitative Data Analysis

In qualitative data analysis, the process of identifying patterns is crucial for extracting meaningful insights. Pattern identification enhances our understanding of complex data by revealing common themes and trends across various data points. Through this investigative approach, researchers can make sense of the diverse responses received during interviews or surveys, ultimately leading to a comprehensive overview of participant perspectives.

Moreover, recognizing these patterns allows for the classification of data into actionable insights. By synthesizing information and visually representing findings, analysts can effectively communicate conclusions and drive decision-making. This vital process not only aids in evaluating previous research but also guides future inquiries, emphasizing the invaluable role of pattern identification in qualitative data analysis.

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• 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

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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Emma J. Chapman, Carole A. Paley & Lucy E. Ziegler

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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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Correspondence to Emma J. Chapman .

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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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