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What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Flexibility

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

Natural settings

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

Meaningful insights

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

Generation of new ideas

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

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

Unreliability

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

Subjectivity

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

Limited generalizability

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

Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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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.

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Qualitative Research Methods: Types, Analysis + Examples

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

What is qualitative research?

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

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

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

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

Gather research insights

Types of qualitative research methods with examples

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

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

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

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

1. One-on-one interview

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

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

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

2. Focus groups

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

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

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

3. Ethnographic research

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

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

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

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

4. Case study research

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

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

5. Record keeping

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

6. Process of observation

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

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

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative research: data collection and analysis

A. qualitative data collection.

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

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

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

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

B. Qualitative data analysis

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

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

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

Characteristics of qualitative research methods

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

Qualitative research method case study

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

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

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

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

When to use qualitative research

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

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

LEARN ABOUT: Steps in Qualitative Research

Qualitative research methods vs quantitative research methods

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

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

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What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Flexibility

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

Natural settings

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

Meaningful insights

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

Generation of new ideas

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

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

Unreliability

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

Subjectivity

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

Limited generalisability

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

Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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The SAGE Encyclopedia of Qualitative Research Methods

Edited by: Lisa M. Given
Publisher: SAGE Publications, Inc.
Publication year: 2008
Online pub date: December 27, 2012
Discipline: Anthropology
Methods: Artistic inquiry , Action research
DOI: https:// doi. org/10.4135/9781412963909
Keywords: art , inquiry Show all Show less
Print ISBN: 9781412941631
Online ISBN: 9781412963909
Buy the book icon link

Reader's guide

Entries a-z, subject index.

Qualitative research is designed to explore the human elements of a given topic, while specific qualitative methods examine how individuals see and experience the world. Qualitative approaches are typically used to explore new phenomena and to capture individuals' thoughts, feelings, or interpretations of meaning and process. Such methods are central to research conducted in education, nursing, sociology, anthropology, information studies, and other disciplines in the humanities, social sciences, and health sciences. Qualitative research projects are informed by a wide range of methodologies and theoretical frameworks.

The SAGE Encyclopedia of Qualitative Research Methods presents current and complete information as well as ready-to-use techniques, facts, and examples from the field of qualitative research in a very accessible style. In taking an interdisciplinary approach, these two volumes target a broad audience and fill a gap in the existing reference literature for a general guide to the core concepts that inform qualitative research practices. The entries cover every major facet of qualitative methods, including access to research participants, data coding, research ethics, the role of theory in qualitative research, and much more—all without overwhelming the informed reader.

Key Features

Defines and explains core concepts, describes the techniques involved in the implementation of qualitative methods, and presents an overview of qualitative approaches to research; Offers many entries that point to substantive debates among qualitative researchers regarding how concepts are labeled and the implications of such labels for how qualitative research is valuedl; Guides readers through the complex landscape of the language of qualitative inquiry; Includes contributors from various countries and disciplines that reflect a diverse spectrum of research approaches from more traditional, positivist approaches, through postmodern, constructionist ones; Presents some entries written in first-person voice and others in third-person voice to reflect the diversity of approaches that define qualitative work

Approaches and Methodologies; Arts-Based Research, Ties to; Computer Software; Data Analysis; Data Collection; Data Types and Characteristics; Dissemination; History of Qualitative Research; Participants; Quantitative Research, Ties to; Research Ethics; Rigor; Textual Analysis, Ties to; Theoretical and Philosophical Frameworks

The SAGE Encyclopedia of Qualitative Research Methods is designed to appeal to undergraduate and graduate students, practitioners, researchers, consultants, and consumers of information across the social sciences, humanities, and health sciences, making it a welcome addition to any academic or public library.

Front Matter

Editorial Board
List of Entries
Reader's Guide
About the Editor
Contributors
Introduction

Reader’s Guide

A/r/tography
Action Research
Advocacy Research
Applied Research
Appreciative Inquiry
Artifact Analysis
Arts-Based Research
Arts-Informed Research
Autobiography
Autoethnography
Basic Research
Clinical Research
Collaborative Research
Community-Based Research
Comparative Research
Content Analysis
Conversation Analysis
Covert Research
Critical Action Research
Critical Arts-Based Inquiry
Critical Discourse Analysis
Critical Ethnography
Critical Hermeneutics
Critical Research
Cross-Cultural Research
Discourse Analysis
Document Analysis
Duoethnography
Ecological Research
Emergent Design
Empirical Research
Empowerment Evaluation
Ethnography
Ethnomethodology
Evaluation Research
Evidence-Based Practice
Explanatory Research
Exploratory Data Analysis
Feminist Research
Field Research
Foucauldian Discourse Analysis
Genealogical Approach
Grounded Theory
Hermeneutics
Heuristic Inquiry
Historical Discourse Analysis
Historical Research
Historiography
Indigenous Research
Institutional Ethnography
Institutional Research
Interdisciplinary Research
Internet in Qualitative Research
Interpretive Inquiry
Interpretive Phenomenology
Interpretive Research
Market Research
Meta-Analysis
Meta-Ethnography
Meta-Synthesis
Methodological Holism Versus Individualism
Methodology
Mixed Methods Research
Multicultural Research
Narrative Analysis
Narrative Genre Analysis
Narrative Inquiry
Naturalistic Inquiry
Observational Research
Oral History
Orientational Perspective
Para-Ethnography
Participatory Action Research (PAR)
Performance Ethnography
Phenomenography
Phenomenology
Place/Space in Qualitative Research
Playbuilding
Portraiture
Program Evaluation
Q Methodology
Readers Theater
Social Justice
Social Network Analysis
Survey Research
Systemic Inquiry
Theatre of the Oppressed
Transformational Methods
Unobtrusive Research
Value-Free Inquiry
Virtual Ethnography
Virtual Research
Visual Ethnography
Visual Narrative Inquiry
Bricolage and Bricoleur
Connoisseurship
Dance in Qualitative Research
Ethnopoetics
Fictional Writing
Film and Video in Qualitative Research
Literature in Qualitative Research
Multimedia in Qualitative Research
Music in Qualitative Research
Photographs in Qualitative Research
Photonovella and Photovoice
Poetry in Qualitative Research
Researcher as Artist
Storytelling
Visual Research
Association for Qualitative Research (AQR)
Center for Interpretive and Qualitative Research
International Association of Qualitative Inquiry
International Institute for Qualitative Methodology
ResearchTalk, Inc.
ATLAS.ti"(Software)
Computer-Assisted Data Analysis
Diction (Software)
Ethnograph (Software)
Framework (Software)
HyperRESEARCH (Software)
MAXqda (Software)
NVivo (Software)
Qualrus (Software)
SuperHyperQual (Software)
TextQuest (Software)
Transana (Software)
Analytic Induction
ATLAS.ti" (Software)
Audience Analysis
Axial Coding
Categorization
Co-Constructed Narrative
Codes and Coding
Coding Frame
Comparative Analysis
Concept Mapping
Conceptual Ordering
Constant Comparison
Context and Contextuality
Context-Centered Knowledge
Core Category
Counternarrative
Creative Writing
Cultural Context
Data Analysis
Data Management
Data Saturation
Descriptive Statistics
Discursive Practice
Diversity Issues
Embodied Knowledge
Emergent Themes
Emic/Etic Distinction
Emotions in Qualitative Research
Ethnographic Content Analysis
Ethnostatistics
Evaluation Criteria
Everyday Life
Experiential Knowledge
Explanation
Gender Issues
Heteroglossia
Historical Context
Horizonalization
Imagination in Qualitative Research
In Vivo Coding
Indexicality
Interpretation
Intertextuality
Liminal Perspective
Literature Review
Lived Experience
Marginalization
Membership Categorization Device Analysis (MCDA)
Memos and Memoing
Meta-Narrative
Negative Case Analysis
Nonverbal Communication
Open Coding
Peer Review
Psychological Generalization
Rapid Assessment Process
Reconstructive Analysis
Recursivity
Reflexivity
Research Diaries and Journals
Research Literature
Researcher as Instrument
Researcher Sensitivity
Response Groups
Rhythmanalysis
Rigor in Qualitative Research
Secondary Analysis
Selective Coding
Situatedness
Social Context
Systematic Sociological Introspection
Tacit Knowledge
Textual Analysis
Thematic Coding and Analysis
Theoretical Memoing
Theoretical Saturation
Thick Description
Transcription
Typological Analysis
Understanding
Video Intervention/Prevention Assessment
Visual Data
Visual Data Displays
Writing Process
Active Listening
Audiorecording
Captive Population
Closed Question
Cognitive Interview
Convenience Sample
Convergent Interviewing
Conversational Interviewing
Covert Observation
Critical Incident Technique
Data Archive
Data Collection
Data Generation
Data Security
Data Storage
Diaries and Journals
Email Interview
Focus Groups
Free Association Narrative Interview
In-Depth Interview
In-Person Interview
Interactive Focus Groups
Interactive Interview
Interview Guide
Interviewing
Leaving the Field
Life Stories
Narrative Interview
Narrative Texts
Natural Setting
Naturalistic Data
Naturalistic Observation
Negotiating Exit
Neutral Question
Neutrality in Qualitative Research
Nonparticipant Observation
Nonprobability Sampling
Observation Schedule
Open-Ended Question
Participant Observation
Peer Debriefing
Pilot Study
Probes and Probing
Projective Techniques
Prolonged Engagement
Psychoanalytically Informed Observation
Purposive Sampling
Quota Sampling
Random Sampling
Recruiting Participants
Research Problem
Research Question
Research Setting
Research Team
Researcher Roles
Researcher Safety
Sample Size
Sampling Frame
Secondary Data
Semi-Structured Interview
Sensitizing Concepts
Serendipity
Snowball Sampling
Stratified Sampling
Structured Interview
Structured Observation
Subjectivity Statement
Telephone Interview
Theoretical Sampling
Triangulation
Unstructured Interview
Unstructured Observation
Videorecording
Virtual Interview
Ethnography (Journal)
Field Methods (Journal)
Forum: Qualitative Social Research (Journal)
International Journal of Qualitative Methods
Journal of Contemporary Ethnography
Journal of Mixed Methods Research
Narrative Inquiry (Journal)
Oral History Review (Journal)
Qualitative Health Research (Journal)
Qualitative Inquiry (Journal)
Qualitative Report, The (Journal)
Qualitative Research (Journal)
Advances in Qualitative Methods Conference
Ethnographic and Qualitative Research Conference
First-Person Voice
Interdisciplinary Qualitative Studies Conference
International Congress of Qualitative Inquiry
International Human Science Research Conference
Publishing and Publication
Qualitative Health Research Conference
Representational Forms of Dissemination
Research Proposal
Education, Qualitative Research in
Evolution of Qualitative Research
Health Sciences, Qualitative Research in
Humanities, Qualitative Research in
Politics of Qualitative Research
Qualitative Research, History of
Social Sciences, Qualitative Research in
Confidentiality
Conflict of Interest
Disengagement
Disinterestedness
Empowerment
Informed Consent
Insider/Outsider Status
Intersubjectivity
Key Informant
Marginalized Populations
Member Check
Over-Rapport
Participant
Participants as Co-Researchers
Reciprocity
Researcher–Participant Relationships
Secondary Participants
Virtual Community
Vulnerability
Generalizability
Objectivity
Probability Sampling
Quantitative Research
Reductionism
Reliability
Replication
Ethics Review Process
Project Management
Qualitative Research Summer Intensive
Research Design
Research Justification
Theoretical Frameworks
Thinking Qualitatively Workshop Conference
Accountability
Authenticity
Ethics and New Media
Ethics Codes
Institutional Review Boards
Integrity in Qualitative Research
Relational Ethics
Sensitive Topics
Audit Trail
Confirmability
Credibility
Dependability
Inter- and Intracoder Reliability
Observer Bias
Subjectivity
Transferability
Translatability
Transparency
Trustworthiness
Verification
Discursive Psychology
Chaos and Complexity Theories
Constructivism
Critical Humanism
Critical Pragmatism
Critical Race Theory
Critical Realism
Critical Theory
Deconstruction
Epistemology
Essentialism
Existentialism
Feminist Epistemology
Grand Narrative
Grand Theory
Nonessentialism
Objectivism
Postcolonialism
Postmodernism
Postpositivism
Postrepresentation
Poststructuralism
Queer Theory
Reality and Multiple Realities
Representation
Social Constructionism
Structuralism
Subjectivism
Symbolic Interactionism

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Research Methods and Design

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a method of research that produces descriptive (non-numerical) data, such as observations of behavior or personal accounts of experiences. The goal of gathering this qualitative data is to examine how individuals can perceive the world from different vantage points. A variety of techniques are subsumed under qualitative research, including content analyses of narratives, in-depth interviews, focus groups, participant observation, and case studies, often conducted in naturalistic settings.

SAGE Research Methods Videos

What questions does qualitative research ask.

A variety of academics discuss the meaning of qualitative research and content analysis. Both hypothetical and actual research projects are used to illustrate concepts.

What makes a good qualitative researcher?

Professor John Creswell analyzes the characteristics of qualitative research and the qualitative researcher. He explains that good qualitative researchers tend to look at the big picture, notice details, and write a lot. He discusses how these characteristics tie into qualitative research.

This is just one segment in a series about qualitative research. You can find the rest of the series in our SAGE database, Research Methods:

Videos covering research methods and statistics

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Next: Mixed Methods Study >>
Last Updated: Feb 6, 2024 9:20 AM

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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.

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Definition of Aggregate and Social Aggregate
Scientific Method Lesson Plan
Definition of a Hypothesis

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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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What is Qualitative in Research

Review Essay
Open access
Published: 28 October 2021
Volume 44 , pages 599–608, ( 2021 )

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Patrik Aspers 1 &
Ugo Corte 2

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In this text we respond and elaborate on the four comments addressing our original article. In that piece we define qualitative research as an “iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied.” In light of the comments, we identify three positions in relation to our contribution: (1) to not define qualitative research; (2) to work with one definition for each study or approach of “qualitative research” which is predominantly left implicit; (3) to systematically define qualitative research. This article elaborates on these positions and argues that a definition is a point of departure for researchers, including those reflecting on, or researching, the fields of qualitative and quantitative research. The proposed definition can be used both as a standard of evaluation as well as a catalyst for discussions on how to evaluate and innovate different styles of work.

What is Qualitative in Qualitative Research

Patrik Aspers & Ugo Corte

What is “Qualitative” in Qualitative Research? Why the Answer Does not Matter but the Question is Important

Mario L. Small

Unsettling Definitions of Qualitative Research

Japonica Brown-Saracino

Avoid common mistakes on your manuscript.

The editors of Qualitative Sociology have given us the opportunity not only to receive comments by a group of particularly qualified scholars who engage with our text in a constructive fashion, but also to reply, and thereby to clarify our position. We have read the four essays that comment on our article What is qualitative in qualitative research (Aspers and Corte 2019 ) with great interest. Japonica Brown-Saracino, Paul Lichterman, Jennifer Reich, and Mario Luis Small agree that what we do is new. We are grateful for the engagement that the four commenters show with our text.

Our article is based on a standard approach: we pose a question drawing on our personal experiences and knowledge of the field, make systematic selections from existing literature, identify, collect and analyze data, read key texts closely, make interpretations, move between theory and evidence to connect them, and ultimately present a definition: “ qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied” (Aspers and Corte 2019 , 139) . We acknowledge that there are different qualitative characteristics of research, meaning that we do not merely operate with a binary code of qualitative versus non-qualitative research. Our definition is an attempt to make a new distinction that clarifies what is qualitative in qualitative research and which is useful to the scientific community. Consequently, our work is in line with the definition that we have proposed.

Given the interest that our contribution has already generated, it is reasonable to argue that the new distinction we put forth is also significant . As researchers we make claims about significance, but it is always the audience—other scientists—who decide whether the contribution is significant or not. Iteration means that one goes back and forth between theory and evidence, and improved understanding refers to the epistemic gains of a study. To achieve this improved understanding by pursuing qualitative research, it is necessary that one gets close to the empirical material. When these four components are combined, we speak of qualitative research.

The four commentators welcome our text, which does not imply that they agree with all of the arguments we advance. In what follows, we single out some of the most important critiques we received and provide a reply aiming to push the conversation about qualitative research forward.

Why a Definition?

We appreciate that all critics have engaged closely with our definition. One main point of convergence between them is that one should not try to define qualitative research. Small ( Forthcoming ) asks rhetorically: “Is producing a single definition a good idea?” He justifies his concern by pointing out that the term is used to describe both different practices (different kinds of studies) and three elements (types of data; data collection, and analysis). Similarly, both Brown-Saracino ( Forthcoming ) and Lichterman, ( Forthcoming ) argue that not only there is no single entity called qualitative research—a view that we share, but instead, that definitions change over time. For Small, producing a single definition for a field as diverse as sociology, or the social sciences for that matter, is restrictive, a point which is also, albeit differently, shared by Brown-Saracino. Brown-Saracino asserts that our endeavor “might calcify boundaries, stifle innovation, and prevent recognition of areas of common ground across areas that many of us have long assumed to be disparate.” Hence, one should not define what is qualitative, because definitions may harm development. Both Small and Brown-Saracino say that we are drawing boundaries between qualitative and quantitative approaches and overstate differences between them. Yet, part of our intent was the opposite: to build bridges between different approaches by arguing that the ‘qualitative’ feature of research pertains both quantitative and qualitative methodologies, which may use and even combine different methods.

In light of these comments we need to elaborate our argument. Moreover, it is important not to maintain hard lines that may lead to scientific tribalism. Nonetheless, the critique of our—or any other definition of qualitative research—typically implies that there is something “there,” but that we have not captured it correctly with our definition. Thus, the critique that we should not define qualitative research comes with an implicit contradiction. If all agree that there is something called “qualitative research,” even if it is only something that is not quantitative, this still presumes that there is something called “qualitative.” Had we done research on any other topic it would probably have been requested by reviewers to define what we are talking about. The same criteria should apply also when we turn the researcher’s gaze on to our own practice.

Moreover, it is doubtful that our commentators would claim that qualitative research can be “anything,” as the more Dadaistic interpretation by Paul Feyerabend ( 1976 ) would have it. But without referring to the realist view of Karl Popper ( 1963 , 232–3) and his ideas of verisimilitude (i.e., that we get close to the truth) we have tried to spell out what we see as an account of the phenomenology of “qualitative.” We identify three positions in relation to the issue of definition of qualitative research:

We should not define qualitative research.

We can work with one definition for each study or approach of “qualitative research,” which is predominantly left implicit.

We can try to systematically define qualitative research.

Obviously, we have embraced and practiced position 3 in reaction to the current state of the field which is largely dominated by position 2--namely that what is qualitative research is open to a large variety of “definitions.” The critical points of our commentators explicitly or implicitly argue in favor of position 1, or perhaps position 2. Our claim that a definition can help researchers sort good from less good research has triggered criticism. Below, we elaborate on this issue.

We maintain that a definition is a valid starting point useful for junior scholars to learn more about what is qualitative and what is quantitative, and for more advanced researchers it may feature as a point of departure to make improvements, for instance, in clarifying their epistemological positions and goals. But we could have done a better job in clarifying our position. Nonetheless, we contend that change and improvement at this late stage of development in social sciences is partially related to and dependent upon pushing against or building upon clear benchmarks, such as the definition that we have formulated. We acknowledge that “definitions might evolve or diversify over time,” as Brown-Saracino suggests. Still, surely social scientists can keep two things in mind at the same time: an existing definition may be useful, but new research may change it. This becomes evident if one applies our definition to the definition itself: our definition is not immune to work that leads to new qualitative distinctions! Having said this, we are happy to see that all four comments profit from getting in close contact with the definition. This means that our definition and the article offer the reader an opportunity to think with (Fine and Corte 2022 ) or, as Small writes, “forces the reader to think.” We believe that both in principle and in practice, we all agree that clarity and definitions are scientific virtues.

What can a Definition Enable?

While we agree with several points in Small’s essay, we disagree on others. Our underlying assumption is that we can build on existing knowledge, albeit not in the way positivism envisioned it. It follows that work which is primarily descriptive, evocative, political, or generally aimed at social change may entail new knowledge, but it does not fit well within the frame within which we operate in this piece. The existence of different kinds of work, each of which relies on different standards of evaluation—which are often unclear and consequential, especially to graduate students and junior scholars (see Corte and Irwin 2017 )—brings us to another point highlighted by both Small and Lichterman: can the definition be used to differentiate good from lesser good kinds of work?

Small argues that while our article promises to develop a standard of evaluation, it fails to do so. We agree: our definition does not specify the exact criteria of what is good and what is poor research. Our definition demarcates qualitative research from non-qualitative by spelling out the qualitative elements of research, which advances a criterion of evaluation. In addition, there is definitely research that meets the characteristics of being qualitative, but that is uninteresting, irrelevant, or essentially useless (see Alvesson et al. 2017 on “gap spotting,” for instance). What is good or not good research is to be decided in an ongoing scientific discussion led by those who actively contribute to the development of a field. A definition, nonetheless, can serve as a point of reference to evaluate scholarly work, and it can also serve as a guideline to demarcate what is qualitative from what it is not.

A Good Definition?

Even if one accepts that there should be a definition of qualitative research, and thinks that such a definition could be useful, it does not follow that one must accept our definition. Small identifies what he sees a paradox in our text, namely that we both speak of qualitative research in general and of qualitative elements in different research activities. The term qualitative, as we note and as Small specifies, is used to describe different things: from small n studies to studies of organizations, states, or other units conceptualized as case studies and analyzed quantitatively as well as qualitatively. We are grateful for this observation, which is correct. We failed to properly address this issue in the original text.

As we discuss in the article, the elements used in our definitions (distinctions, process, closeness, and improved understanding) are present in all kinds of research, even quantitative. Perhaps the title of our article should have been: “What is Qualitative in Research?” Our position is that only when all the elements of the definition are applied can one speak of qualitative research. Hence, the first order constructs (i.e., the constructs the actors in the field have made) (Aspers 2009 ) of, for example, “qualitative observations,” may indeed refer to observations that make qualitative distinction in the Aristotelian sense on which we rely. Still, if these qualitative observations are commensurated with a ratio-scale (i.e., get reduced to numbers) this research can no longer be called “qualitative.” It is for this reason that we say that, to refer to first order constructs, “quantitative” research processes entail “qualitative” elements. This research is, as it were, partially qualitative, but it is not, taken together, qualitative research. Brown-Saracino raises a similar point in relation to her own and others works that combine “qualitative” and “quantitative” research. We do not think that one is inherently better, yet we agree with the general idea that qualitative research is particularly useful in identifying research questions and formulating theories (distinctions) that, at a later point should, when possible, be tested quantitatively on larger samples (cf. Small 2005 ). It is our hope that, with our clarification above, it will be easier for researchers to understand what one is and what one is not doing. We also hope that our study will stimulate further dialogue and collaboration between researchers who primarily work within different traditions.

Small wonders if a researcher who tries to replicate a “qualitative” study (according to our definition) is doing qualitative research. The person is certainly doing research, and some elements are likely conducted in a qualitative fashion according to our definition, for example if the method of in-depth fieldwork is employed. But regardless of the method used, and regardless of whether the person finds new things, if the result is binary coded as either confirming or disconfirming existing research, qualitative research is not being conducted because no new distinction is offered. Imagine the same study being replicated for the 20 th time. Surely the researcher must use the same “qualitative” methods (to use the first order construct). It may even excite a large academic audience, but it would not count as qualitative research according to our definition. Our definition requires both that the research process has made use of all its elements, but it also requires the acceptance by the audience. Having said this, in practice, it is more likely that such a study would also report new distinctions that are acknowledged by an audience. If such a study is reviewed and published, these are additional indicators that the new distinctions are considered significant, at least to some extent: how much research space it opens up, and how much it helps other researchers continue the discussion by formulating their own questions and making their own claims (Collins 1998 , 31), whether by agreeing with it by applying it, by refining it (Snow et al. 2003 ), or by disagreeing and identifying new ways forward. There are two key characteristics that make a contribution relevant: newness and usefulness (Csikszentmihalyi 1996 ), both of which are related to the established state of knowledge within a field. Relatedly, Small asks: “Is newness enough? What does a new distinction that does not improve understanding look like?” There are also other indicators that demarcate whether a contribution is significant and to what extent. Some of these indicators include the number of citations a piece of work generates, the reputation of the journal or press where the work is published, and how widely the contribution is used—for instance, across specializations within the same discipline, or across different fields (i.e., different ways of valuation and evaluation) (Aspers and Beckert 2011 ) of scientific output. In principle, if a contribution ends up being used in an area where it would have unlikely been used, then one may further argue for its significance.

As it is implicit in our work when we talk about distinctions, we refer to theory building, albeit appreciating different conceptualizations and uses of the term theory (Abend 2008 ) and ways to achieve it (e.g., Zerubavel 2020 ). Brown-Saracino writes that our project may hold “the unintended consequence of limiting exploratory research designs and methodological innovations.” While we cannot predict the impact of our research, we are certainly in favor of experimentation and different styles of work. In line with David Snow, Calvin Morrill and Leon Anderson ( 2003 , 184), we argue that many qualitative researchers start their projects being underprepared in theory and theory development, oftentimes with the goal of describing, and leaving alone the black box of theory, or postponing it to later phases of the project. Our definition, along with the work by those authors and others on theory development, can be one way to heighten the chances researchers can make distinctions and develop theory.

Lichterman argues that we are not giving enough weight to interpretation and that we should relate more strongly to the larger project of the Geistenwissenschaften . We agree that interpretation is a key element in qualitative research, and we draw on Hans-Georg Gadamer ( 1988 ) who refined the idea of the hermeneutic circle.

Another critique, raised by Reich ( Forthcoming ), is that positionality is a key element of qualitative research. That in working towards a definition, we have “overlooked much of the methodological writings and contributions of women, scholars of color, and queer scholars” that could have enriched our definition, especially regarding “getting closer to the phenomenon studied.” Surely, the way we have searched for and included references means that we have ‘excluded’ the vast majority of research and researchers who do qualitative work. However, we have not included texts by some authors in our sample based on any specific characteristics or according to any specific position. This critique is valid only if Reich shows more explicitly what this inclusion would add to our definition.

Though we agree with much of what Reich says, for example about the role of bodies and reflexivity in ethnographic work, the idea of positionality as a normative notion is problematic. At least since Gadamer wrote in the early 1960s (1988), it is clear that there are no interpretations ‘from nowhere.’ Who one is cannot be bracketed in an interpretation of what has occurred. The scientific value of this more identity- and positionality-oriented research that accounts also of the positionality of the interpreter, is essentially already well acknowledged. Reflection is not just something that qualitative researcher do; it is a general aspect of research. Ethnographic researchers may need certain skills to get close and understand the phenomenon they study, yet they also need to maintain distance. As Fine and Hallett write: “The ethnographic stranger is uniquely positioned to be a broker in connecting the field with the academy, bringing the site into theory and, perhaps, permitting the academy to consider joint action with previously distant actors” (Fine and Hallett 2014 , 195). Moreover, Brown-Saracino illustrates well what it means to get close, and we too see that ethnography, in various forms and ways, is useful as other qualitative activities. Though ethnographic research cannot be quantitative, qualitative work is broader than solely ethnographic research. Furthermore, reflexivity is not something that one has to do when doing qualitative research, but something one does as a researcher.

Reich’s second point is more important. The claim is that if the standpoint-oriented argument is completely accepted, it will most likely violate what we see as the essence of research. We warned in our article that qualitative research may be treated as less scientific than quantitative within academia, but also in the general public, if too many in academia claim to be doing “qualitative research” while they are in fact telling stories, engaging in activism, or writing like journalists. Such approaches are extra problematic if only some people with certain characteristics are viewed as the only legitimate producers of certain types of knowledge. If these tendencies are fueled, it is not merely the definition of “qualitative” that is at stake, but what the great majority see as research in general. Science cannot reach “The Truth,” but if one gives up the idea communal and universal nature of scientific knowledge production and even a pragmatic notion of truth, much of its value and rationale of science as an independent sphere in society is lost (Merton 1973 ; Weber 1985 ). Ralf Dahrendorf framed this form of publicness by writing that: “Science is always a concert, a contrapuntal chorus of the many who are engaged in it. Insofar as truth exists at all, it exists not as a possession of the individual scholar, but as the net result of scientific interchange” (1968, 242–3). The issue of knowledge is a serious matter, but it is also another debate which relates to social sciences being low consensus fields (Collins 1994 ; Fuchs 1992 ; Parker and Corte 2017 , 276) in which the proliferation of journals and lack of agreement about common definitions, research methods, and interpretations of data contributes to knowledge fragmentation. To abandon the idea of community may also cause confusion, and piecemeal contributions while affording academics a means to communicate with a restricted in-group who speak their own small language and share their views among others of the same tribe, but without neither the risk nor possibility of gaining general public recognition. In contrast, we see knowledge as something public, that, ideal-typically, “can be seen and heard by everybody” (Arendt 1988 , 50), reflecting a pragmatic consensual approach to knowledge, but with this argument we are way beyond the theme of our article.

Our concern with qualitative research was triggered by the external critique of what is qualitative research and current debates in social science. Our definition, which deliberately tries to avoid making the use of a specific method or technique the essence of qualitative, can be used as a point of reference. In all the replies by Brown-Saracino, Lichterman, Reich, and Small, several examples of practices that are in line with our definition are given. Thus, the definition can be used to understand the practice of research, but it would also allow researchers to deliberately deviate from it and develop it. We are happy to see that all commentators have used our definition to move further, and in this pragmatic way the definition has already proved its value.

New research should be devoted to delineating standards and measures of evaluation for different kinds of work such as the those we have identified above: theoretical, descriptive, evocative, political, or aimed at social change (see Brady and Collier 2004; Ragin et al. 2004 ; Van Maanen 2011 ). And those standards could respectively be based upon scientific or stylistic advancement and social and societal impact. Footnote 1 Different work should be evaluated in relation to their respective canons, goals, and audiences, and there is certainly much to gain from learning from other perspectives. Relatedly, being fully aware of the research logics of both qualitative and quantitative traditions (Small 2005 ) is also an advantage for improving both of them and to spur further collaboration. Bringing further clarity on these points will ultimately improve different traditions, foster creativity potentially leading to innovative projects, and be useful both to younger researchers and established scholars.

The last two terms refer to whether the impacts are more micro as related to agency, or macro, as related to structural changes. An example of the latter kind is Matthew Desmond’s Eviction (2016) having substantial societal impact on public policy discussions, raising and researching a broader range of housing issues in the US. A case of the former is Arlie Hochchild’s studies on emotional labor of women in the workplace (1983) and her more recent book on the alienation of white, working-class Americans (2016).

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Acknowledgements

The authors are grateful for comments by Gary Alan Fine, Jukka Gronow, and John Parker.

Open access funding provided by University of St. Gallen. The research reported here is funded by University of St. Gallen, Switzerland and University of Stavanger, Norway.

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Aspers, P., Corte, U. What is Qualitative in Research. Qual Sociol 44 , 599–608 (2021). https://doi.org/10.1007/s11133-021-09497-w

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Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as 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 stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain 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 important 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, qualitative and quantitative work are not necessarily opposites nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are 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 that there is 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 together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. 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.” As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. In essence, Grounded Theory’s goal is to explain for example 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 defined as the “study of the meaning of phenomena or the study of the particular”. At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. 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 whereas Phenomenology focuses on describing and explaining an event or phenomena 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 ‘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, in the hopes of creating a cohesive story, or narrative. 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”.

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. It is valuable for researchers, both qualitative and quantitative, 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 ontology 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 the work researchers do. It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers 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 vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully 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 it could be approximated. 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. 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 constructivist as well, meaning they think there is no objective external reality that exists but rather 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”. Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.”

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 and can even change the role of the researcher themselves. For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own 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 at play. The following are examples of participant sampling and selection:

Purposive sampling- selection based on the researcher’s rationale in terms of 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 is 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 a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, 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 with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo.

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. Results also could be in the form of themes and theory or model development.

Dissemination

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

Examples of Application

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 for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good 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 there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized 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 both why teens start to smoke as well as 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 non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

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

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly 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 of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the 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 of the park, 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 the 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 population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others.

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

Publication types

Study Guide

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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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

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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Home » Qualitative Research: Definition, Methods and Examples

Qualitative Research: Definition, Methods and Examples

May 31, 2023 max 4min read.

This article covers:

What Is Qualitative Research?

Qualitative research methods, qualitative research analysis, qualitative research findings, advantages and disadvantages of qualitative research, examples of qualitative research, qualitative research definition:.

Qualitative research is a type of research methodology that aims to understand and interpret social phenomena by exploring the subjective experiences, perspectives, and meanings of individuals or groups. It focuses on gathering in-depth, non-numerical data through interviews, observations, and analysis of texts or artifacts.

Qualitative research is a valuable approach used in various fields to understand human behavior, attitudes, and experiences deeply. Unlike quantitative research , which focuses on numerical data and statistical analysis, qualitative research explores the subjective aspects of a phenomenon, providing rich and nuanced insights.

It allows researchers to gather detailed and context-specific information through interviews, observations, and focus groups. This methodology is particularly useful when investigating complex social phenomena, exploring new areas of study, or when quantitative data alone may not capture the full range of human experiences.

Here are some commonly used qualitative research methods:

Face-to-Face Interviews

This method involves one-on-one interviews between the researcher and the participant. It allows in-depth exploration of the participant’s thoughts, experiences, and perspectives.

Focus Groups

Focus groups involve a small group of participants (typically 6-12) who engage in a guided discussion facilitated by a researcher. This method promotes interaction and enables researchers to observe group dynamics.

Ethnographic Research

Ethnography involves immersing the researcher in the participants’ natural environment to understand their culture, practices, and behaviors. Researchers may observe and interact with participants over an extended period to gain insights.

Case Study Research

Case studies examine a particular individual, group, organization, or situation. Researchers collect and analyze data from various sources, such as interviews, documents, and observations, to gain an in-depth understanding.

Record Keeping

Researchers may keep detailed records, such as field notes, journals, or memos, during the research process. These records capture their observations, reflections, and emerging insights.

Qualitative Observation

This method directly observes and documents participants’ behaviors and interactions in their natural settings. Researchers may take field notes or use audiovisual recordings to capture relevant details.

Qualitative research analysis is interpreting and making sense of qualitative data collected during a research study. It involves systematically examining and analyzing textual, visual, or audio data to uncover patterns, themes, and meanings that emerge from the data.

Here are some key steps involved in qualitative research analysis:

Data Preparation

Organize and transcribe the collected data, whether it is interviews, focus group discussions, field notes, or other forms of qualitative data.

Familiarization

Immerse yourself in the data to become familiar with the content and gain a deeper understanding of the context in which the data was collected. Read and re-read the data to identify initial impressions and patterns.

Begin the coding process by systematically labeling and categorizing segments of the data. Coding can be done using different approaches, such as inductive coding (allowing themes to emerge from the data) or deductive coding (applying pre-defined categories or theories).

Theme Development

Group related codes together to form themes or patterns. Look for connections and relationships within and between themes. Refine and revise the themes as you continue analyzing the data.

Data Interpretation

Analyze the themes and patterns in depth, considering their implications and meanings. Explore the relationships between different themes and sub-themes. Use theoretical frameworks or relevant literature to help interpret and make sense of the findings.

Triangulation

Validate the findings by comparing and contrasting different data sources or perspectives.

Qualitative research findings offer valuable insights into the complex nuances of human experiences and perspectives. Once the data has been collected and analyzed, it is essential to utilize these findings effectively.

Firstly, researchers can organize and categorize the data thematically, identifying common patterns, themes, or trends that emerge from the analysis. This process helps in summarizing and interpreting the richness of qualitative data.

Next, the findings can be used to develop comprehensive and vivid narratives, enabling a deeper understanding of the research topic.

Additionally, researchers can compare and contrast their findings with existing theories or prior research to contribute to the existing knowledge base. The implications of the findings can also be highlighted, addressing practical applications or potential areas for further exploration.

Finally, communicating the findings to relevant stakeholders , such as academic communities, policymakers, or practitioners, is crucial for disseminating knowledge, promoting dialogue, and influencing decision-making processes .

Advantages :

In-depth understanding of complex phenomena
Flexibility and adaptability allow researchers to adapt their approach during the research process.
Qualitative research emphasizes the social and cultural context in which phenomena occur.
Qualitative research acknowledges that researchers’ interpretations and subjectivity play a crucial role in shaping findings.
Emergent design, meaning that the research design evolves as new insights emerge.

Disadvantages :

Limited generalizability
Qualitative research is susceptible to researcher bias, as researchers’ interpretations and subjectivity influence the findings.
Time-consuming and resource-intensive. Qualitative research requires substantial time and resources.
Analyzing qualitative data involves interpretation, and researchers may arrive at different conclusions.
Qualitative research primarily focuses on descriptive and narrative data rather than numerical or statistical measurements.

As a product manager , qualitative research can involve user interviews to gather insights about your target users’ needs and preferences.

For example, when developing a fitness app, you would interview fitness enthusiasts to understand their current app usage, pain points, and desired features. Analyzing the interview data helps you make informed decisions to create a user-centric product that addresses their needs and enhances user satisfaction.

More like this:

What Is User Research? Definition and Overview
What Is Research and development (R&D)? The Overview
How the hook model works?
Market Research: Definition, Process and Techniques

Qualitative and quantitative approaches are used in research and data analysis.

Qualitative refers to non-numerical data that focuses on exploring subjective experiences, opinions, and meanings. It involves methods such as interviews, observations, and open-ended surveys.

On the other hand, quantitative deals with numerical data and relies on statistical analysis. It involves collecting data through structured surveys, experiments, or measurements. The focus is on objective observations, patterns, and statistical relationships.

Here is a quick list of characteristics of qualitative research:

acknowledges and embraces the subjective nature of human experiences and interpretations.

examines phenomena within their natural settings and considers the context in which they occur.

It uses inductive reasoning, where theories and concepts emerge from the data collected rather than being predetermined.

allows for flexibility in study design, data collection methods, and analysis to adapt to the evolving research process.

Rather than statistical data, it focuses on non-numerical data, such as words, images, and observations.

Qualitative research aims to understand and explore social phenomena in depth through methods like interviews, observations, focus groups, or document analysis.

On the other hand, quantitative research focuses on quantifying and measuring phenomena using methods such as surveys, experiments, or systematic observations.

Mixed methods research combines qualitative and quantitative approaches, using both data collection and analysis methods to provide a more comprehensive understanding of the research topic.

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

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

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

What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

Biographies

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

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

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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Research Fundamentals March 25, 2024

Mixed Methods Research

Navigating the rich tapestry of human behavior research often feels like being an explorer without a map—exciting, yes, but also a bit bewildering. Enter Mixed Methods Research (MMR), the compass that promises to guide through the intricate dance of numbers and narratives. This approach doesn’t just ask us to look at the stars and the sand but to see the desert and the galaxy as parts of a whole. So, if you’ve ever found yourself pondering the vast universe of human behavior with a statistical chart in one hand and a collection of personal stories in the other, wondering how to bridge the divide, you’re in the right place.

Introduction to mixed methods research, definition and overview of mixed methods in research.

Mixed Methods Research represents a methodological approach that integrates both quantitative and qualitative research techniques within a single study or series of studies. Its core premise lies in the belief that by combining quantitative (numeric, statistical data) and qualitative (textual, narrative data) approaches, researchers can gain a more comprehensive understanding of research problems than by using either method in isolation.

The essence of Mixed Methods Research is not merely in the simultaneous use of quantitative and qualitative data; it’s in the integration, analysis, and interpretation of these data types to provide a more robust and nuanced perspective on research questions. This integration allows for a deeper dive into complex phenomena, unveiling layers of understanding that might remain obscured under a mono-methodological lens. In the context of human behavior research, this approach proves particularly potent, as it enables the exploration of not just the ‘how’ and ‘what’ but also the ‘why’ behind human actions, thoughts, and interactions.

At its heart, Mixed Methods Research is characterized by its methodological pluralism. It draws on the strengths and mitigates the weaknesses of both quantitative and qualitative research, offering a versatile toolkit for tackling the multifaceted nature of human behavior. Through this approach, researchers can capture the breadth and depth of human experiences, making it an invaluable strategy in studies aiming to address complex, interdisciplinary questions.

The application of Mixed Methods goes beyond mere data collection. It influences all phases of a research project—from the formulation of research questions to the design of the study, data collection and analysis, and the interpretation of results. This methodological approach fosters a dynamic interplay between numbers and narratives, allowing for a fuller, more holistic understanding of research phenomena.

Mixed Methods Research is also inherently pragmatic. It is guided by the research question(s) at hand, rather than by the philosophical debates that often distinguish quantitative from qualitative research. This pragmatism encourages flexibility and innovation in research design and implementation, making Mixed Methods an increasingly popular choice among researchers across various disciplines, including social sciences, health sciences, education, and beyond.

In sum, Mixed Methods Research is a powerful paradigm that embraces the complexity of the real world, offering a comprehensive framework for understanding human behavior in its most authentic and multifaceted form. Its interdisciplinary nature and emphasis on integration make it a cornerstone of contemporary research efforts aimed at answering some of the most pressing questions about human nature and society.

The Evolution of Mixed Methods Research

The evolution of Mixed Methods Research (MMR) reflects a broadening understanding of the research process and a growing recognition of the value of integrating diverse methodological perspectives. This evolution can be traced through several key phases, each marked by shifts in how researchers conceptualized the relationship between quantitative and qualitative methods, and by changes in the broader academic and social context that influenced research practices.

Early Foundations and Dichotomy (Pre-1970s): Historically, the quantitative and qualitative research paradigms were often seen as diametrically opposed or mutually exclusive, with fierce debates surrounding their relative merits and applicability. Quantitative methods, rooted in positivism, were typically associated with the natural sciences and emphasized measurement, objectivity, and statistical analysis. Qualitative methods, on the other hand, drew from constructivist and interpretivist traditions, focusing on understanding human experience, meaning, and context through detailed, narrative data.

Emergence of Mixed Methods (1970s-1980s): The late 20th century witnessed a gradual softening of the rigid boundaries between quantitative and qualitative research. Scholars began to argue for the potential benefits of combining methods, suggesting that such integration could enhance the depth and breadth of understanding in social science research. This period saw the publication of seminal works that laid the conceptual groundwork for Mixed Methods Research, although it had not yet become widely recognized or formalized as a distinct methodological approach.

Expansion and Formalization (1990s-2000s): The 1990s and early 2000s marked a significant period of growth and formalization for Mixed Methods Research. Key figures in the field, such as John W. Creswell , introduced comprehensive frameworks and designs for conducting MMR, helping to establish it as a legitimate and valuable research approach. During this time, the academic community saw an increasing number of publications, dedicated journal issues, and the formation of professional organizations focusing on Mixed Methods. This era was characterized by a burgeoning recognition of the pragmatic advantages of MMR, as researchers sought to address complex questions that required both the generalizability of quantitative data and the depth of qualitative insights.

Maturation and Diversification (2010s-Present): The most recent phase in the evolution of Mixed Methods Research is marked by its maturation and diversification. MMR is now widely accepted across various disciplines, from education and health sciences to business and environmental studies. The approach has become more sophisticated, with researchers developing innovative designs and strategies for integrating quantitative and qualitative components. Technological advances have facilitated the collection, analysis, and integration of mixed data types, further expanding the possibilities for MMR.

Moreover, the current landscape of Mixed Methods Research is characterized by a growing emphasis on cultural sensitivity, ethical considerations, and the co-construction of knowledge with research participants. There’s an increasing awareness of the role of context in shaping research inquiries and outcomes, driving scholars to adopt Mixed Methods approaches that are not only methodologically rigorous but also socially and ethically responsive.

The evolution of Mixed Methods Research reflects a broader shift towards epistemological pluralism and methodological innovation in the pursuit of comprehensive, nuanced understandings of complex phenomena. As MMR continues to evolve, it holds the promise of further enriching the research landscape by bridging gaps between disciplines, methodologies, and ways of knowing.

Importance of Mixed Methods in Human Behavior Research

The significance of Mixed Methods Research (MMR) in the study of human behavior cannot be overstated. This approach plays a critical role in unraveling the complexities of human thoughts, actions, and interactions, offering insights that might remain elusive under single-method approaches. The importance of MMR in this domain is multifaceted, touching on the depth and breadth of understanding, the nuance of analysis, and the practical application of findings.

Comprehensive Insights: One of the most compelling advantages of MMR is its ability to provide a more complete and nuanced picture of human behavior. By integrating quantitative data, which offers breadth and generalizability, with qualitative insights, which provide depth and context, researchers can achieve a more holistic understanding of their subject matter. This comprehensive view is particularly crucial in human behavior research, where the interplay of various factors—psychological, social, cultural, environmental—can significantly influence outcomes.

Enhanced Validity: Mixed Methods Research enhances the validity of study findings through methodological triangulation. By examining a research question from multiple angles and through different methodological lenses, researchers can corroborate their results, increasing confidence in their conclusions. In the context of human behavior research, where variables are often complex and interdependent, such triangulation is invaluable for ensuring that the findings are robust and reflective of real-world phenomena.

Flexibility and Innovation: MMR’s inherent flexibility encourages innovation in research design and implementation. Researchers are not confined to a single methodological pathway but can adapt their approaches based on the research question and the evolving context of their study. This adaptability is particularly beneficial in human behavior research, where dynamic and rapidly changing social landscapes may necessitate shifts in focus or methodology to capture relevant phenomena accurately.

Addressing Complexity: Human behavior is inherently complex, influenced by an array of internal and external factors. MMR allows researchers to explore this complexity in a nuanced manner, combining quantitative measures of behavior (e.g., frequency, patterns) with qualitative understandings of motivations, perceptions, and experiences. This capacity to address both the “how much” and “why” aspects of human behavior is crucial for developing comprehensive theories and models that reflect the intricacy of human life.

Informing Practice and Policy: Findings from Mixed Methods Research are particularly relevant for informing practice and policy in fields concerned with human behavior, such as psychology, education, public health, and social work. The integration of quantitative and qualitative data provides a strong evidence base for developing interventions, programs, and policies that are grounded in a deep understanding of the target population’s needs, preferences, and experiences. This relevance is especially pronounced in applied research, where the goal is to effect positive change or improvement in individuals’ lives or societal structures.

Facilitating Interdisciplinary Research: Finally, the use of MMR fosters interdisciplinary collaboration, drawing together expertise from various fields to address complex questions about human behavior. This collaborative potential is essential for tackling the multifaceted challenges facing society today, from mental health and educational attainment to social inequality and environmental behavior. By bridging disciplinary divides, MMR contributes to a more integrated and collaborative approach to understanding and addressing the nuances of human behavior.

In summary, the importance of Mixed Methods Research in the study of human behavior lies in its ability to merge quantitative breadth with qualitative depth, enhance validity through triangulation, provide flexible and innovative research designs, and offer comprehensive insights into complex phenomena. These attributes make MMR an indispensable tool in the quest to understand and improve the human condition.

Theoretical Foundations of Mixed Methods Research

Key concepts and terminologies.

Mixed Methods Research (MMR) is underpinned by a variety of key concepts and terminologies that elucidate its principles and practices. Understanding these terms is essential for grasping the theoretical foundations of MMR and appreciating its utility in research. Some of the most critical concepts include triangulation, complementarity, integration, methodology, and paradigms, among others. Here, we explore these terms to provide a foundational understanding of MMR.

Triangulation: Originally derived from navigation and military strategy, triangulation in the context of MMR refers to the use of multiple methods, data sources, researchers, or theoretical perspectives to cross-check and validate findings. This multifaceted approach enhances the reliability and validity of research outcomes, ensuring that the results are not merely artifacts of a specific method or data source. In human behavior research, triangulation allows for a more comprehensive understanding of phenomena by examining them from different angles.

Complementarity: This concept speaks to the idea that qualitative and quantitative methods can be used together to provide complementary insights into a research question. While quantitative data might quantify the extent or frequency of certain behaviors, qualitative data can shed light on the experiences, meanings, and contexts behind these behaviors. Complementarity underscores the value of integrating diverse data forms to achieve a richer, more nuanced understanding of research subjects.

Integration: At the heart of MMR is the integration of quantitative and qualitative data. Integration can occur at various stages of the research process, including study design, data collection, analysis, and interpretation. The goal is to create a cohesive narrative or framework that bridges numeric outcomes and narrative insights, offering a holistic view of the research question. Effective integration requires careful planning and a clear rationale for how and why different data types are combined.

Methodology: In the context of MMR, methodology refers to the overarching strategy or plan that guides the selection and use of specific methods in a research study. It encompasses the rationale for employing MMR, the design of the study, and the procedures for collecting, analyzing, and integrating quantitative and qualitative data. Methodology is closely linked to the research question and objectives, shaping the approach to inquiry and analysis.

Methodological Pluralism: This term acknowledges the legitimacy and value of multiple methods of inquiry. Methodological pluralism is a core principle of MMR, reflecting a stance that no single method can fully capture the complexity of human behavior. By embracing a variety of methods, researchers can access different dimensions of their subjects, enriching the research process and outcomes.

Pragmatism: Pragmatism is a philosophical framework often associated with MMR. It posits that the value of research lies in its practical applications and outcomes, rather than in adhering to strict philosophical dichotomies between positivism and constructivism. From a pragmatic perspective, the choice of research methods should be driven by the research question and the practical implications of the findings, rather than by ideological commitments to a particular method or paradigm.

Sequential, Concurrent, and Transformative Designs: These terms describe common frameworks for structuring Mixed Methods studies. Sequential designs involve conducting one phase of research (qualitative or quantitative) followed by another, using the results from the first phase to inform the second. Concurrent designs involve conducting qualitative and quantitative research simultaneously and integrating the findings. Transformative designs prioritize a theoretical or conceptual framework, using MMR to address issues of power, inequality, or social justice.

Understanding these key concepts and terminologies is fundamental to appreciating the depth and breadth of Mixed Methods Research. They provide the theoretical scaffolding that supports the practical application of MMR, guiding researchers in designing, conducting, and interpreting studies that seek to illuminate the complexities of human behavior and other phenomena.

Philosophical Underpinnings and Paradigms

Mixed Methods Research (MMR) is not merely a combination of research techniques but is deeply rooted in specific philosophical underpinnings and paradigms that inform its principles, strategies, and goals. Understanding these philosophical foundations is crucial for comprehending the rationale behind MMR, its implementation, and its potential to generate rich, multifaceted insights into research questions. Among the most influential philosophical underpinnings of MMR are pragmatism, constructivism, positivism, and post-positivism, each contributing unique perspectives and justifications for the use of mixed methods.

Pragmatism: Pragmatism is often cited as the primary philosophical foundation for MMR. It is a practical, action-oriented philosophy that values research methods based on their utility in answering specific research questions and solving problems. Pragmatism eschews the dichotomy between positivist and constructivist paradigms, suggesting instead that the choice of research methods should be driven by the research objectives and the practical implications of the findings. This perspective encourages flexibility and openness in research design, allowing researchers to select and integrate quantitative and qualitative methods in a manner that best addresses the complexity of the research problem.

Constructivism: Constructivism posits that reality is socially constructed, emphasizing the importance of human experiences, meanings, and interpretations. From a constructivist viewpoint, qualitative methods are particularly valuable for exploring individuals’ perspectives, contexts, and interactions. In MMR, constructivist principles can guide the qualitative components of a study, helping to uncover the depth and richness of human experiences and the ways in which people make sense of their world.

Positivism and Post-Positivism: Positivism, with its roots in the natural sciences, advocates for objective measurement, hypothesis testing, and the search for universal laws. Post-positivism, acknowledging the limitations of pure objectivity, still emphasizes rigorous methods and empirical evidence but accepts that knowledge is tentative and theory-laden. In MMR, positivist and post-positivist paradigms often inform the quantitative aspects of a study, focusing on measurement, causality, and generalization. These paradigms provide a framework for testing hypotheses and quantifying variables, offering a counterbalance to the interpretive depth of qualitative analysis.

Transformative Paradigms: Transformative paradigms, including critical theory, feminism, and participatory approaches, focus on power dynamics, equity, and social justice. These paradigms advocate for research that challenges societal structures, addresses issues of marginalization, and seeks to bring about change. In MMR, transformative paradigms can guide the research process towards ethical engagement, reflexivity, and the inclusion of diverse and often underrepresented voices, ensuring that the study contributes to broader social and political objectives.

Dialectical Pluralism: Dialectical pluralism is an approach that acknowledges and engages with the diversity of philosophical perspectives within MMR. It encourages researchers to reflect on and articulate their own epistemological and methodological assumptions, fostering dialogue between different paradigms. This reflective process enhances the rigor and depth of MMR, promoting a thoughtful integration of quantitative and qualitative methods that respects the complexity of the research problem.

The philosophical underpinnings and paradigms of MMR provide a rich theoretical landscape that informs its practice. By grounding research in these philosophical traditions, MMR leverages the strengths of diverse methodological approaches, enabling researchers to navigate the complexities of human behavior and social phenomena with nuance and insight. This philosophical diversity not only enriches the research process but also ensures that MMR can effectively address a wide range of questions, contributing to the advancement of knowledge across disciplines.

Differences and Similarities Between Quantitative and Qualitative Research

Understanding the distinctions and connections between quantitative and qualitative research is essential for effectively implementing Mixed Methods Research (MMR). While these two approaches are often seen as fundamentally different, recognizing their complementary strengths is key to appreciating the rationale behind combining them in MMR. Below, we explore the primary differences and similarities between quantitative and qualitative research, highlighting how each contributes uniquely to the comprehensive insights sought in MMR.

Differences:

Nature of Data: Quantitative research deals with numerical data that can be quantified and subjected to statistical analysis. In contrast, qualitative research focuses on non-numerical data, such as words, images, or observations, aiming to understand concepts, thoughts, or experiences in depth.
Research Objectives: Quantitative research often aims to test hypotheses, measure variables, and determine relationships or causality between them. Qualitative research, on the other hand, seeks to explore meanings, patterns, and descriptions of phenomena, usually in a more open-ended and exploratory manner.
Methodological Approach: Quantitative methods typically involve structured procedures and instruments, such as surveys or experiments, to collect data that can be generalized to larger populations. Qualitative methods rely on more flexible, open-ended techniques, such as interviews or participant observation, focusing on gaining deep insights into specific contexts or groups.
Analysis and Interpretation: In quantitative research, data analysis involves statistical operations to identify patterns, trends, or differences. Qualitative analysis, however, is interpretive and iterative, aiming to identify themes, narratives, and meanings within the data.

Similarities:

Objective Inquiry: Both approaches strive for objectivity in their own ways. Quantitative research emphasizes objectivity through measurement and statistical analysis, while qualitative research seeks to minimize bias through reflexivity and rigorous data interpretation methods.
Contribution to Understanding: Each method can contribute to a comprehensive understanding of research questions. Quantitative research provides the breadth and generalizability of findings, whereas qualitative research offers depth and context, enriching the interpretation of results.
Research Design Considerations: Both require careful research design, including clear formulation of research questions, appropriate selection of methodologies, and ethical considerations regarding participants and data.
Iterative Process: Although their processes differ, both approaches involve iterative stages of data collection, analysis, and interpretation. Researchers may refine their methods, questions, or focus based on preliminary findings, whether they are statistical trends or emerging themes.
Empirical Evidence: At their core, both methodologies seek to generate empirical evidence to support insights, conclusions, or theories. The nature of this evidence may differ, but the commitment to empiricism unites both approaches.

Integrating Quantitative and Qualitative Research in MMR:

The integration of quantitative and qualitative research in MMR leverages the strengths of both to provide a more comprehensive understanding of research questions. Quantitative data’s breadth and generalizability, combined with qualitative data’s depth and context, offer a holistic view that neither approach could achieve alone. This synergy allows MMR to address complex, multifaceted research questions, particularly in the study of human behavior, where both the measurable aspects of phenomena and the underlying meanings and interpretations are crucial for a full understanding.

In summary, while quantitative and qualitative research differ in their focus, methods, and types of data they produce, both are essential for exploring the multifaceted nature of research questions. MMR capitalizes on these differences and similarities, facilitating a robust, multidimensional approach to research that is greater than the sum of its parts.

When to Use Mixed Methods Research

Mixed Methods Research (MMR) offers a versatile approach for exploring complex questions by integrating quantitative and qualitative data. However, its application should be thoughtfully considered based on the research objectives, questions, and context. Below is a concise guide for determining when to use MMR, including key questions researchers can ask themselves and situations where MMR might not be the best approach.

When to Use MMR:

MMR is particularly suitable in scenarios where researchers seek to:

Achieve a comprehensive understanding of a research problem by exploring both the breadth (quantitative data) and depth (qualitative data) of the phenomenon.
Examine complex phenomena that cannot be fully understood through quantitative or qualitative methods alone due to their multifaceted nature.
Validate or cross-check findings from one method with another to enhance the reliability and validity of the results.
Develop and test new instruments or measures where initial qualitative research informs the creation of quantitative instruments or vice versa.
Understand contexts or mechanisms that explain quantitative results, providing a richer understanding of statistical trends or outliers.
Bridge the gap between theory and practice by grounding theoretical insights in empirical data and real-world applications.

Questions to Ask Before Choosing MMR:

To decide if MMR is appropriate for your research, consider the following questions:

Does my research question have both exploratory (‘why’, ‘how’) and confirmatory (‘how much’, ‘how many’) components?
Could the integration of qualitative and quantitative data provide insights that would not be possible through a single method?
Am I seeking to understand a phenomenon in depth, including its context, diversity, and complexity?
Do I have the resources (time, skills, budget) to effectively conduct both qualitative and quantitative research?
Is there value in cross-validating my findings through multiple data sources or perspectives?

When Not to Use MMR:

While MMR has broad applicability, there are situations where it might not be the best choice:

Limited Resources: If resources are too constrained to support the rigorous application of both qualitative and quantitative methods, it may be better to choose a single method that can be applied thoroughly.
Clear Methodological Preference: If the research question can be comprehensively addressed through either quantitative or qualitative methods alone, and there is no clear added value in integrating both.
Lack of Expertise: If there is a lack of expertise in either quantitative or qualitative methods, and acquiring this expertise or collaborating with others is not feasible.
Research Simplicity: For straightforward research questions that do not require the depth and breadth provided by MMR.

Choosing whether to use Mixed Methods Research depends on a careful assessment of your research goals, questions, and resources. While MMR can provide rich, comprehensive insights into complex phenomena, it requires thoughtful planning, a clear rationale for methodological integration, and the resources to conduct both components of the research effectively.

Designing Mixed Methods Research

Overview of mixed methods designs.

Mixed Methods Research (MMR) designs are strategic frameworks that guide the integration of quantitative and qualitative components within a study. These designs enable researchers to leverage the strengths of both approaches to achieve a comprehensive understanding of research questions. MMR designs can vary widely depending on the research objectives, the nature of the research question, and the specific way in which the quantitative and qualitative components are integrated. Three primary designs—explanatory sequential, exploratory sequential, and convergent parallel—serve as foundational structures for most MMR studies. Each design has unique characteristics, applications, and processes for integrating data.

Explanatory Sequential Design:

The explanatory sequential design is characterized by two distinct phases. The first phase involves collecting and analyzing quantitative data. The findings from this phase then inform the second phase, which involves qualitative data collection and analysis. The purpose of this design is often to explain or expand upon quantitative results by exploring participants’ perspectives, motivations, and experiences in more detail. For instance, a researcher might use a survey to identify trends or patterns (quantitative phase) and then conduct interviews to understand the reasons behind these trends (qualitative phase). This design is particularly useful when initial quantitative results require further explanation or contextualization.

Exploratory Sequential Design:

In contrast to the explanatory sequential design, the exploratory sequential design begins with qualitative data collection and analysis, followed by quantitative data collection and analysis. This design is used when a researcher seeks to explore a phenomenon, develop instruments, or identify variables that can later be tested or measured quantitatively. The qualitative phase might involve conducting focus groups or interviews to gather insights into a topic, which then guide the development of a survey instrument or hypothesis for the subsequent quantitative phase. This design is ideal for new or under-researched areas where preliminary qualitative insights are necessary to inform the structure and focus of quantitative investigation.

Convergent Parallel Design:

The convergent parallel design involves simultaneously conducting quantitative and qualitative data collection and analysis, with the two strands of data kept independent. After the separate analyses, the results are compared, contrasted, or combined to draw comprehensive conclusions. This design allows for a robust examination of research questions from both numerical and narrative perspectives, providing a multifaceted view of the subject matter. It is particularly useful when researchers aim to validate or corroborate findings across methodologies or when they seek to present a well-rounded understanding of a phenomenon by integrating diverse types of data.

Choosing a Design:

Selecting the appropriate MMR design depends on several factors, including the research question, objectives, resources, and the theoretical framework guiding the study. Researchers must consider what they aim to achieve with their study—whether it is to explain, explore, or understand a phenomenon from multiple angles—and how best to integrate quantitative and qualitative methods to accomplish those goals.

Each MMR design offers distinct advantages and addresses different research needs. The explanatory sequential design is powerful for delving deeper into quantitative findings, while the exploratory sequential design is invaluable for generating hypotheses or measures from qualitative insights. The convergent parallel design, meanwhile, allows for a comprehensive examination of research questions by comparing and synthesizing diverse data types.

Ultimately, the choice of MMR design is a critical decision that shapes the research process, influencing data collection, analysis, and interpretation. By carefully selecting a design that aligns with their research questions and objectives, scholars can effectively harness the strengths of both quantitative and qualitative methods, producing rich, nuanced insights that advance understanding in their field.

Criteria for Selecting an Appropriate Mixed Methods Design

Choosing the right Mixed Methods Research (MMR) design is pivotal to the success of a study. The selection process should be informed by a set of criteria that aligns with the research objectives, questions, and the overall context of the investigation. Below are essential criteria to consider when selecting an appropriate MMR design:

1. Research Objectives and Questions:

Objective Alignment: The chosen design must align with the primary objectives of the study. Whether the aim is to explain quantitative results, explore a phenomenon to develop a survey, or understand a research problem from multiple perspectives, the MMR design should facilitate these aims.
Question Complexity: Complex research questions that involve understanding phenomena from diverse angles may benefit from a convergent parallel design, while more straightforward questions focused on explaining or exploring may be suited to sequential designs.

2. Theoretical Framework:

The theoretical or conceptual framework guiding the study can influence the choice of MMR design. For example, a study grounded in a transformative framework may opt for a design that emphasizes qualitative insights to explore issues of power or injustice, potentially leading to an exploratory sequential design where qualitative findings inform subsequent quantitative inquiry.

3. Resources and Feasibility:

Practical considerations such as time, budget, and available expertise can significantly impact the choice of design. For instance, sequential designs, which require two phases of data collection and analysis, might be more time-consuming and resource-intensive than convergent parallel designs, where data are collected and analyzed simultaneously.

4. Data Integration:

The plan for how quantitative and qualitative data will be integrated is crucial in selecting an MMR design. Consider whether integration will occur at the data collection, analysis, or interpretation phase, and choose a design that facilitates this integration effectively.

5. Validity and Reliability Concerns:

Different MMR designs offer various strategies for enhancing the validity and reliability of the study findings. For example, an explanatory sequential design may allow for validating quantitative findings with qualitative insights, while a convergent parallel design might provide a way to cross-validate findings from both strands of the research.

6. Audience and Dissemination:

Consider the expectations and preferences of the anticipated audience for the research findings. Some disciplines may favor certain MMR designs over others, or there may be specific expectations for how findings should be integrated and presented.

7. Ethical Considerations:

The ethical implications of the research design, including considerations related to participant burden, confidentiality, and the representation of diverse perspectives, should also guide the selection of an MMR design. Designs should be chosen and implemented in ways that respect the rights and dignity of participants.

8. Pilot Studies and Preliminary Research:

Initial findings from pilot studies or preliminary research can inform the choice of MMR design. For instance, early qualitative insights might suggest the need for a more extensive quantitative follow-up, pointing towards an exploratory sequential design.

Selecting the appropriate MMR design is a critical step that requires careful consideration of these criteria. The decision should be driven by the research aims, theoretical orientation, practical constraints, and ethical considerations, ensuring that the chosen design optimally supports the study’s goals. By systematically evaluating these criteria, researchers can select a Mixed Methods design that not only addresses their research questions comprehensively but also maximizes the potential for meaningful and impactful findings.

Steps in Planning and Implementing Mixed Methods Research

Planning and implementing Mixed Methods Research (MMR) involves a systematic approach to ensure that both quantitative and qualitative components are effectively integrated to address the research question comprehensively. The following steps outline a general process for planning and executing an MMR study, from conceptualization to dissemination:

Define the Research Problem and Questions: Clearly articulate the research problem and develop specific research questions that Mixed Methods are well-suited to address. Consider how integrating quantitative and qualitative approaches can provide a more comprehensive understanding than either approach alone.
Review Literature and Theoretical Frameworks: Conduct a thorough literature review to understand the current state of knowledge and theoretical perspectives related to the research problem. This review can help identify gaps in knowledge that MMR could address and inform the selection of a theoretical framework.
Choose an Appropriate Mixed Methods Design: Select a Mixed Methods design (e.g., explanatory sequential, exploratory sequential, convergent parallel) based on the research objectives, questions, and theoretical framework. Consider how the design facilitates the integration of quantitative and qualitative data to answer the research questions.
Develop a Detailed Research Plan: Outline the procedures for both the quantitative and qualitative strands of the study, including sample selection, data collection methods, and analysis plans. Ensure that the plan includes detailed steps for integrating the two strands at the appropriate stages of the research process.
Address Ethical Considerations: Consider ethical issues related to conducting MMR, such as informed consent, confidentiality, and the handling of sensitive data. Obtain approval from relevant ethics committees or institutional review boards.
Collect Data: Implement the data collection plan, adhering to the procedures outlined for both quantitative and qualitative components. Be flexible and responsive to unforeseen issues that may arise during data collection.
Analyze Data: Analyze the quantitative and qualitative data according to the plans established in the research design. Consider the use of software tools that can facilitate the management and analysis of mixed data types.
Integrate Findings: Integrate the findings from the quantitative and qualitative strands of the study. The method of integration will depend on the chosen Mixed Methods design and might involve comparing, contrasting, or combining results to draw comprehensive conclusions.
Interpret Results in Context: Interpret the integrated findings in the context of the research questions, theoretical framework, and existing literature. Consider the implications of the findings for theory, practice, and future research.
Disseminate Findings: Prepare and present the research findings in a manner that effectively communicates the integration of quantitative and qualitative results. Consider multiple dissemination channels, such as academic journals, conferences, and stakeholder reports, ensuring that the chosen outlets are appropriate for the study’s audience.
Reflect on the Research Process: Engage in reflexivity by critically reflecting on the research process, including the integration of methods and the challenges encountered. Consider how the insights gained from this process can inform future Mixed Methods research.

Planning and implementing MMR is a complex but rewarding process that allows researchers to explore research questions with a depth and breadth that would not be possible with a single method alone. By following these steps, researchers can ensure a rigorous and thoughtful approach to integrating quantitative and qualitative methods, ultimately enriching the understanding of complex phenomena.

Applications of Mixed Methods in Human Behavior Research

Case studies highlighting the use of mixed methods in various disciplines.

Mixed Methods Research (MMR) has been successfully applied across a variety of disciplines to explore complex human behavior phenomena. The following case studies, drawn from psychology, sociology, and education, exemplify the versatility and depth that MMR can bring to research.

1. Psychology: Understanding Mental Health Interventions

This study utilized a convergent parallel design to examine the effectiveness of different mental health interventions. Quantitative data were gathered through standardized psychological assessments to measure changes in mental health symptoms, while qualitative data were collected via patient interviews to explore their experiences and perceptions of the interventions. The integration of these data types provided a holistic understanding of the interventions’ impact, revealing not only their statistical effectiveness but also how patients experienced the treatment process, including factors contributing to or hindering their recovery.

2. Sociology: Exploring Social Networks and Community Support

Hesse-Biber, S. N. (2010). Mixed Methods Research: Merging Theory with Practice. Guilford Press.

In a study focused on the dynamics of social networks and community support in urban neighborhoods, researchers employed an exploratory sequential design. Initial qualitative data were collected through ethnographic observations and interviews with community members to understand the nature and significance of social ties. These insights then informed the development of a survey instrument that was used to quantitatively assess the prevalence and impact of these social networks across a larger population. The study provided nuanced insights into how social connections influence community resilience and individual well-being.

3. Education: Examining Educational Interventions for At-Risk Youth

Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage.

Researchers used an explanatory sequential design to evaluate the effectiveness of educational interventions for at-risk youth. The first phase involved quantitative assessment of academic outcomes using standardized test scores to identify the interventions’ impacts. Subsequently, qualitative interviews with students, teachers, and parents were conducted to explore the contextual factors influencing the effectiveness of these interventions. This mixed methods approach revealed not only the interventions’ outcomes but also the complex interplay of individual, familial, and institutional factors affecting students’ educational trajectories.

These case studies illustrate the power of MMR to provide comprehensive insights into human behavior by leveraging the strengths of both quantitative and qualitative research. By combining numerical data with narrative depth, researchers are able to uncover not just patterns of behavior but also the meanings and contexts that underpin these patterns. This dual focus on the macro and micro aspects of human behavior makes MMR particularly valuable in disciplines concerned with understanding the complexities of human actions and experiences.

Advantages of Using Mixed Methods for Studying Complex Human Behaviors

Mixed Methods Research (MMR) offers a unique set of advantages for investigating the multifaceted nature of human behaviors, which are often too complex to be captured fully by either quantitative or qualitative methods alone. The integration of these approaches within MMR provides a more nuanced and comprehensive understanding of human behaviors, contributing significantly to the field of behavioral research. Key advantages include:

Comprehensive Insights: MMR allows researchers to explore human behavior from multiple dimensions—combining the breadth and generalizability of quantitative data with the depth and context provided by qualitative insights. This comprehensive approach can uncover the underlying mechanisms of behavior, providing a fuller understanding than either method could on its own.

Enhanced Validity: By employing both quantitative and qualitative methods, MMR can enhance the validity of research findings through triangulation—verifying results through multiple sources of data. This cross-validation strengthens the reliability of the conclusions drawn about human behavior.

Flexibility: MMR’s inherent flexibility supports the exploration of unexpected findings in real time. Researchers can adapt their methodologies in response to preliminary results, exploring new avenues of inquiry as they emerge and thus capturing the complexity of human behavior more effectively.

Increased Impact: The dual approach of MMR facilitates research that is both academically rigorous and practically relevant. By grounding theoretical insights in empirical data and real-world contexts, MMR studies can inform policy-making, intervention design, and best practices in fields related to human behavior.

Rich Data for Theory Development: MMR’s capacity to generate rich, layered data supports the development and refinement of theories related to human behavior. Integrating quantitative patterns with qualitative narratives allows for a dynamic interplay between theory and data, fostering theoretical advancements that are deeply grounded in empirical evidence.

Engagement with Diverse Perspectives: Through qualitative components, MMR can capture the voices and perspectives of diverse populations, including those that are often marginalized or overlooked in purely quantitative research. This inclusivity enriches the understanding of human behavior in its varied forms and contexts.

How Mixed Methods Can Bridge the Gap Between Theory and Practice

MMR not only enhances our understanding of complex human behaviors but also plays a crucial role in bridging the gap between theoretical research and practical application. This bridging function is achieved through several key mechanisms:

Translating Findings into Action: By providing a holistic understanding of research problems, MMR equips practitioners and policymakers with the nuanced insights needed to develop effective interventions, programs, and policies. The integration of quantitative and qualitative data ensures that these actions are based on both statistical evidence and contextual understanding.
Informing Practice with Theory: MMR studies often generate findings that challenge or refine existing theories, contributing to the theoretical evolution in various disciplines. By grounding these theoretical developments in mixed-methods evidence, MMR ensures that practice is informed by up-to-date, empirically supported theories.
Identifying Practical Implications of Theoretical Constructs: Through its qualitative component, MMR can explore how theoretical constructs play out in real-world settings, revealing the practical implications of abstract theories. This process helps to make theory more accessible and actionable for practitioners.
Facilitating Collaborative Research: MMR encourages collaboration between researchers from different disciplinary backgrounds, bridging theoretical divides and fostering interdisciplinary approaches to complex problems. This collaboration can lead to more robust research designs that are capable of addressing both theoretical questions and practical concerns.
Empowering Stakeholders: By involving stakeholders in the research process, particularly during the qualitative phases of MMR, researchers can ensure that their studies address relevant, real-world issues. This stakeholder engagement helps to translate theoretical research findings into practical solutions that meet the needs of those affected by the research.

In sum, Mixed Methods Research not only enriches our understanding of human behavior through its methodological rigor and comprehensive approach but also serves as a vital conduit between theory and practice. By effectively integrating quantitative breadth with qualitative depth, MMR facilitates the development of research that is both theoretically sound and practically relevant, thereby enhancing the impact of behavioral research on society.

Data Collection and Analysis in Mixed Methods Research

Techniques for collecting quantitative and qualitative data.

Mixed Methods Research (MMR) utilizes a variety of data collection techniques to gather both quantitative and qualitative data, offering a comprehensive view of the research subject. The incorporation of biometric data sources and the use of advanced research platforms like iMotions Lab can significantly enhance the depth and quality of data collected. Below, we explore a range of techniques for collecting quantitative and qualitative data, including the role of biometric measures and technology-enhanced methods.

Quantitative Data Collection Techniques:

Surveys and Questionnaires: Structured instruments designed to collect numerical data on participants’ attitudes, behaviors, or characteristics. These can be administered online, in person, or by phone.
Standardized Tests: Used particularly in educational research, standardized tests provide quantitative data on abilities, proficiency, or psychological traits.
Biometric Measurements: Biometric data, including heart rate , skin conductance , eye tracking , and facial expression analysis , offer objective, physiological insights into emotional and cognitive responses. Platforms like iMotions Lab facilitate the integration of these data sources into research designs, enabling researchers to quantify complex emotional and cognitive reactions.
Behavioral Observations: Quantified observations of behavior, often coded according to a predefined schema, can provide quantitative data on frequency, duration, and types of behaviors in natural or controlled settings.

Qualitative Data Collection Techniques:

Interviews: Semi-structured or unstructured interviews allow for in-depth exploration of participants’ experiences, perceptions, and motivations. Interviews can be conducted face-to-face, over the phone, or via video conferencing.
Focus Groups: Group discussions that provide insights into social dynamics, attitudes, and perceptions among a group of participants, offering depth and context beyond individual perspectives.
Ethnography: Extended observation and immersion in a community or group provide detailed, contextualized understandings of social processes, cultures, and behaviors.
Case Studies: In-depth analysis of a single case or a small number of cases, which can provide detailed insight into complex phenomena in real-life contexts.
Document Analysis: Examination of documents, texts, and other forms of communication to understand themes, discourses, or historical trends related to the research question.

Enhancing MMR with Biometric and Technological Methods:

Platforms like iMotions Lab offer powerful tools for enhancing MMR by integrating biometric data with traditional quantitative and qualitative methods. By capturing physiological responses, researchers can add a layer of objective data that enriches their understanding of participants’ reactions and behaviors. For instance, eye tracking can reveal attention patterns that participants themselves might not be aware of or able to articulate, while facial expression analysis can provide insights into emotional responses without the need for self-report.

The use of such technologies supports a multimodal approach to data collection, allowing researchers to correlate physiological measures with self-reported data, behavioral observations, and qualitative insights. This integration can uncover nuanced relationships between physiological states, cognitive processes, and subjective experiences, offering a more holistic view of human behavior.

In summary, a diverse array of data collection techniques is available to Mixed Methods researchers, each contributing unique insights into the phenomena under study. The strategic combination of these methods, especially with the inclusion of biometric data and advanced research platforms like iMotions Lab, can significantly enhance the richness and depth of the research, providing a robust framework for understanding complex human behaviors.

Integration of Data: Connecting, Merging, or Embedding Quantitative and Qualitative Data

The integration of quantitative and qualitative data is a hallmark of Mixed Methods Research (MMR), allowing researchers to draw comprehensive conclusions that neither method could achieve alone. This integration can take various forms, including connecting, merging, or embedding data, each offering unique advantages for enhancing understanding. Effective data integration involves systematic strategies to combine or relate the quantitative and qualitative components of a study, thereby enriching the research findings and insights. Here, we explore these key strategies for data integration in MMR.

Connecting Data:

Connecting involves using the results from one method to inform or enhance the other, typically in sequential designs. For example, initial qualitative findings might identify key themes or variables that are then tested or quantified through quantitative methods. Conversely, quantitative data might reveal patterns or anomalies that are further explored through qualitative inquiry. This approach ensures that the insights gained from one method directly influence the application and interpretation of the other, creating a cohesive narrative that bridges both datasets.

Merging Data:

Merging data involves bringing together quantitative and qualitative data sets to analyze them as a whole, often seen in convergent parallel designs. Researchers simultaneously collect and analyze both types of data, then compare or combine the results in the interpretation phase. Merging allows for a direct comparison or juxtaposition of qualitative and quantitative findings, highlighting convergences and divergences that can provide a more nuanced understanding of the research question. For example, statistical trends identified in survey responses can be enriched and contextualized by personal narratives from interviews, offering a fuller picture of the phenomena under study.

Embedding Data:

Embedding refers to the integration of one type of data within the framework primarily guided by the other, often used to address specific aspects of the research question or to bring additional insights into a predominantly quantitative or qualitative study. For instance, a mainly quantitative study might embed qualitative elements to provide depth to statistical findings, or a primarily qualitative study might incorporate quantitative data for grounding in broader patterns or trends. This approach allows researchers to maintain a clear focus on their primary methodological approach while enriching it with insights from the other method.

Strategies for Effective Data Integration:

Develop a Clear Rationale: Begin with a clear rationale for integrating data, specifying how this approach will address the research question more effectively than using a single method.
Plan for Integration: Integration should be planned from the outset of the study, with clear steps outlined for how and when the quantitative and qualitative data will be connected, merged, or embedded.
Use Integrative Tools and Techniques: Employ tools and techniques that facilitate data integration, such as data matrices, joint displays, or narrative structures that weave together quantitative and qualitative findings.
Ensure Methodological Rigor: Maintain rigor in both quantitative and qualitative components of the study, ensuring that each stands on its own merits while contributing to the integrated analysis.
Reflect on the Integration Process: Throughout the research process, continually reflect on how the integration of data is contributing to the understanding of the research question, and adjust strategies as needed to enhance coherence and depth.

Integration is a critical step in MMR that maximizes the strengths of both quantitative and qualitative methods. By thoughtfully connecting, merging, or embedding data, researchers can achieve a more complete and nuanced understanding of complex phenomena, ultimately enhancing the quality and impact of their research findings.

Tools and Software Useful for Mixed Methods Data Analysis

The analysis of Mixed Methods Research (MMR) data requires tools and software capable of handling both quantitative and qualitative datasets, as well as facilitating their integration. The choice of software can significantly impact the efficiency, depth, and rigor of data analysis in MMR. Below, we discuss several tools and software programs that are particularly useful for analyzing mixed methods data.

Quantitative Data Analysis Software:

SPSS (Statistical Package for the Social Sciences): Widely used for statistical analysis in social science, SPSS is user-friendly and capable of handling a wide range of statistical procedures, making it suitable for analyzing the quantitative component of MMR studies.
Stata: Known for its powerful statistical capabilities, Stata is suitable for data management, statistical analysis, and graphics, supporting researchers in complex quantitative data analysis.
R: An open-source software environment for statistical computing and graphics, R offers extensive capabilities for data manipulation, calculation, and graphical display, catering to advanced quantitative analysis needs.

Qualitative Data Analysis Software:

NVivo: NVivo supports qualitative and mixed methods research by enabling the organization, analysis, and visualization of non-numerical or unstructured data, such as interviews, open-ended survey responses, articles, social media, and web content.
ATLAS.ti: Offering a suite of tools for qualitative data analysis, ATLAS.ti facilitates the coding, retrieval, and analysis of text-based data, along with powerful querying and visualization features.
MAXQDA: This software supports qualitative, quantitative, and mixed methods research. It provides tools for coding, content analysis, discourse analysis, and visual tools for data mapping and analysis.

Software for Biometric and Physiological Data Analysis:

iMotions Lab: iMotions is a multimodal research platform that integrates data from biometric sensors, including eye tracking , facial expression analysis , EEG (electroencephalography) , GSR (galvanic skin response) , and heart rate monitoring , among others. It provides a comprehensive solution for collecting and analyzing physiological data in conjunction with traditional quantitative and qualitative data, enhancing the depth and validity of MMR studies. iMotions Lab is particularly valuable for research that seeks to understand emotional, cognitive, and physical responses in a variety of contexts.

Software for Data Integration and Visualization:

Dedoose: An online application designed for managing, analyzing, and presenting qualitative and mixed methods research data. Dedoose offers features for easy integration of quantitative and qualitative data, with tools for mixed methods analysis and visualization.
Tableau: While primarily a data visualization tool, Tableau can be useful in mixed methods research for creating interactive and shareable dashboards that combine quantitative data metrics with qualitative insights.
Microsoft Excel: A versatile tool that, while not specifically designed for MMR, can be used effectively for preliminary data analysis, integration, and visualization, especially for researchers familiar with its advanced features.

Selecting the right mix of tools and software for MMR data analysis depends on the specific needs of the research project, including the complexity of the data, the level of integration required, and the researcher’s familiarity with the software. These tools can significantly enhance the researcher’s ability to analyze and integrate diverse data types, offering richer insights and more robust conclusions in mixed methods studies.

Challenges and Solutions in Mixed Methods Research

Common challenges faced by researchers.

Mixed Methods Research (MMR) offers a comprehensive approach to exploring complex research questions by integrating quantitative and qualitative methodologies. Despite its strengths, MMR presents unique challenges that researchers must navigate. These challenges include methodological biases, resource constraints, integrating diverse data types, and maintaining methodological rigor across both quantitative and qualitative components. Below, we discuss these common challenges and outline potential solutions to address them.

Methodological Biases:

Challenge: Researchers may have a preference or bias towards either quantitative or qualitative methods, influenced by their training or the dominant research culture in their field. This bias can affect the design, implementation, and interpretation of MMR studies.
Solution: Encourage interdisciplinary collaboration and training to expose researchers to the strengths and limitations of both methodologies. Adopting a pragmatic research stance that prioritizes the research question over methodological preferences can also help mitigate biases.

Resource Constraints:

Challenge: MMR can be resource-intensive, requiring significant time, financial resources, and expertise in both quantitative and qualitative methods. These constraints can be particularly challenging for early-career researchers or those working in under-resourced institutions.
Solution: Careful planning and prioritization of research activities can help optimize resource use. Researchers might also consider leveraging existing datasets, collaborating with colleagues from different disciplines, or seeking external funding to support their projects.

Integrating Diverse Data Types:

Challenge: Integrating quantitative and qualitative data in a meaningful way poses conceptual and practical challenges. Researchers must determine how to relate these data types to each other and to the overarching research question.
Solution: Develop a clear plan for data integration at the outset of the study, specifying how and when integration will occur. Utilize frameworks and tools designed for MMR data integration, such as joint displays or mixed methods matrices, to facilitate this process.

Maintaining Methodological Rigor:

Challenge: Ensuring rigor in both quantitative and qualitative components of an MMR study can be demanding, as each requires adherence to distinct criteria for validity and reliability.
Solution: Adopt established criteria for rigor in quantitative and qualitative research, and apply these systematically throughout the study. Engage in reflexivity, peer debriefing, and transparent reporting to enhance the study’s credibility and trustworthiness.

Navigating Complex Ethical Considerations:

Challenge: MMR may involve complex ethical considerations, especially when researching vulnerable populations or sensitive topics. Balancing the ethical demands of quantitative and qualitative methodologies can be challenging.
Solution: Develop a comprehensive ethical framework that addresses potential concerns across both components of the study. Seek guidance from institutional review boards (IRBs) early in the research process, and ensure ongoing informed consent and participant confidentiality.

Training and Expertise:

Challenge: Conducting MMR requires expertise in both quantitative and qualitative research methods, which can be a significant barrier for researchers trained primarily in one methodological tradition.
Solution: Seek out interdisciplinary training opportunities, workshops, and courses that offer instruction in both sets of methods. Collaborating with colleagues from diverse methodological backgrounds can also provide valuable learning experiences and support.

By recognizing and proactively addressing these challenges, researchers can enhance the quality and impact of their Mixed Methods Research, leveraging its full potential to explore complex research questions in a comprehensive and nuanced manner.

Ethical Considerations in Mixed Methods Research

Mixed Methods Research (MMR) encompasses unique ethical considerations due to its integration of quantitative and qualitative methodologies. Navigating these ethical landscapes requires a nuanced understanding of the implications associated with both types of research. Below, we explore key ethical considerations specific to MMR and suggest strategies to address them responsibly.

Informed Consent:

Consideration: Ensuring informed consent in MMR can be complex, particularly when the research design evolves over time, as in sequential mixed methods studies. Participants must be adequately informed about the nature of both quantitative and qualitative components, including any potential risks or benefits.
Strategy: Provide clear, comprehensive consent forms that explain the study’s aims, methods, and any potential risks or benefits. Update consent forms as the study evolves and ensure participants understand they can withdraw at any time without penalty.

Confidentiality and Anonymity:

Consideration: Maintaining confidentiality and anonymity can be challenging, especially in qualitative components involving detailed narratives that might inadvertently reveal participants’ identities.
Strategy: Implement strict data management protocols to safeguard participant information. Use pseudonyms and remove identifying details from qualitative data. Ensure secure storage of data and limit access to the research team.

Cultural Sensitivity:

Consideration: MMR often explores diverse cultural contexts, raising the need for cultural sensitivity and awareness. Ethical research practices must respect cultural norms and values, particularly in qualitative research involving participant observation or interviews.
Strategy: Engage with cultural experts or community leaders as part of the research process. Employ culturally sensitive research methods and consider involving participants in the design and implementation of the study to ensure it respects cultural norms.

Data Integration and Interpretation:

Consideration: The integration and interpretation of quantitative and qualitative data raise ethical considerations related to the representation and weighting of different data types. There is a risk of privileging one type of data over another or misrepresenting the findings through inappropriate integration.
Strategy: Approach data integration with methodological rigor and transparency. Clearly document the process of integration and interpretation, ensuring that both quantitative and qualitative data are represented accurately and fairly.

Participant Burden:

Consideration: MMR can increase the burden on participants, particularly in designs that require involvement in multiple phases of data collection or lengthy qualitative interviews.
Strategy: Carefully consider the design and implementation of the study to minimize participant burden. Provide clear explanations of what participation entails, offer flexible scheduling options, and consider compensating participants for their time.

Reporting and Dissemination:

Consideration: Ethical reporting in MMR involves presenting integrated findings in a way that respects the complexity and nuances of the data while avoiding misinterpretation or oversimplification.
Strategy: Ensure that the reporting of findings transparently reflects the integration of quantitative and qualitative data. Highlight the contributions of both data types to the study’s conclusions and discuss any limitations or uncertainties.

Addressing these ethical considerations in Mixed Methods Research requires thoughtful planning, sensitivity to participants’ needs and contexts, and a commitment to ethical principles throughout the research process. By adopting responsible strategies, researchers can conduct MMR that is not only methodologically sound but also ethically robust, contributing valuable insights while respecting the dignity and rights of all participants.

Reporting and Evaluating Mixed Methods Research

Guidelines for reporting mixed methods research findings.

Reporting the findings of Mixed Methods Research (MMR) requires a structured approach that clearly communicates how quantitative and qualitative components were integrated and the insights derived from this integration. The complexity of MMR findings necessitates clarity, transparency, and a comprehensive presentation that respects the integrity of both methodological strands. Below are guidelines for effectively reporting MMR findings:

1. Introduction and Rationale:

Begin by clearly stating the research problem, objectives, and the rationale for using a mixed methods approach. Explain how the integration of quantitative and qualitative methods addresses the research question more effectively than either method alone.

2. Methodology Description:

Provide a detailed description of the mixed methods design used in the study, including the specific type of design (e.g., explanatory sequential, exploratory sequential, convergent parallel) and the rationale for its selection.
Describe the quantitative and qualitative data collection and analysis procedures, ensuring to detail how these components were planned to be integrated from the study’s inception.

3. Data Integration:

Clearly explain the process of integrating quantitative and qualitative data. This could involve the use of joint displays, narrative structures, or other techniques to combine or compare data sets.
Illustrate how this integration contributed to answering the research question, highlighting the added value of combining methodological approaches.

4. Findings:

Present the findings in a way that reflects the integration of quantitative and qualitative data. This may involve juxtaposing quantitative results with qualitative insights, weaving together narratives and statistics, or presenting them in parallel to demonstrate convergence or divergence.
Use visual aids, such as tables, graphs, and figures, to help illustrate complex data relationships and support the narrative.

5. Discussion:

Interpret the findings in the context of existing literature, theory, and the stated research questions. Discuss how the mixed methods approach provided a more comprehensive understanding of the topic.
Acknowledge any limitations of the study and suggest areas for future research.

6. Conclusion:

Summarize the key findings and their implications for theory, practice, or policy. Emphasize the contribution of the mixed methods approach to the research field.

A reference that provides a comprehensive overview of guidelines for reporting MMR findings is the Journal of Mixed Methods Research (SAGE Publications) , which offers articles and resources dedicated to the methodological development and practical application of mixed methods research.

Criteria for Evaluating the Quality of Mixed Methods Research

Evaluating the quality of MMR involves assessing both the rigor of the quantitative and qualitative components and the effectiveness of their integration. Key criteria include:

1. Methodological Rigor:

Evaluate the quality and appropriateness of the quantitative and qualitative methods used, including the reliability and validity of instruments, the credibility and dependability of qualitative findings, and adherence to ethical standards.

2. Integration:

Assess the extent and effectiveness of the integration of quantitative and qualitative data. Successful integration should enhance understanding beyond what is possible with a single method.

3. Coherence:

The research design, implementation, and findings should demonstrate coherence and alignment with the research question and objectives. The mixed methods design should be clearly justified and applied consistently throughout the study.

4. Completeness:

The study should fully address the research questions, with the mixed methods approach providing comprehensive insights that cover the breadth and depth of the topic.

5. Transparency and Replicability:

The report should clearly document the research process, including data collection and analysis procedures, so that the study can be understood, evaluated, and potentially replicated by others.

6. Contribution to Knowledge:

Evaluate the study’s contribution to advancing knowledge in the field, including how the mixed methods approach added value to understanding the research problem.

These criteria are essential for ensuring the quality and impact of Mixed Methods Research. Evaluators and researchers alike should consider these aspects when conducting, reporting, and assessing MMR studies to uphold the integrity and contribute meaningfully to the body of research within their fields.

Examples of Impactful Mixed Methods Research Articles/Publications

Mixed Methods Research (MMR) has made significant contributions across various fields, offering insights that enrich understanding and inform practice. Below are examples of impactful MMR studies, spanning different disciplines, along with their citations. These examples illustrate the versatility and depth of mixed methods approaches in addressing complex research questions.

1. Health Sciences:

Citation: Creswell, J. W., Klassen, A. C., Plano Clark, V. L., & Smith, K. C. for the Office of Behavioral and Social Sciences Research. (2011). Best practices for Mixed Methods Research in the Health Sciences. National Institutes of Health. https://obssr.od.nih.gov/sites/obssr/files/Best_Practices_for_Mixed_Methods_Research.pdf

This seminal publication outlines best practices for conducting MMR in health sciences, providing guidelines that have been widely adopted by researchers studying complex health-related phenomena.

2. Education:

Citation: Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255-274.

https://doi.org/10.2307/1163620

This foundational article proposes a conceptual framework for mixed-method evaluation designs in education, influencing subsequent generations of educational researchers in their approach to integrating quantitative and qualitative methods.

3. Social Sciences:

Citation: Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1(2), 112-133.

https://doi.org/10.1177/1558689806298224

This article offers a comprehensive definition of mixed methods research, discussing its foundations, applications, and implications for social sciences research. It has become a key reference point for scholars engaging in MMR.

4. Psychology:

Citation: Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544. https://doi.org/10.1007%2Fs10488-013-0528-y

This article discusses the use of purposeful sampling in qualitative research as part of a mixed methods study, illustrating its application in the context of mental health services research. The study demonstrates how qualitative components can enrich and contextualize quantitative findings.

5. Environmental Studies:

Citation: Bryman, A. (2006). Integrating quantitative and qualitative research: how is it done? Qualitative Research, 6(1), 97-113. https://doi.org/10.1177/1468794106058877

Although not strictly limited to environmental studies, this article provides an in-depth examination of how quantitative and qualitative research can be integrated, with examples that include environmental research. It highlights the methodological challenges and solutions in conducting MMR, contributing to a broader understanding of mixed methods applications in environmental contexts.

These examples reflect the broad applicability and impact of Mixed Methods Research across disciplines. Each study showcases the unique contribution of MMR to advancing knowledge, addressing complex questions, and bridging the gap between quantitative breadth and qualitative depth.

Future Directions in Mixed Methods Research

Emerging trends and innovations in mixed methods research.

The field of Mixed Methods Research (MMR) is continually evolving, with new trends and innovations enhancing its scope, depth, and applicability across disciplines. An area of significant growth involves the integration of biometric research, leveraging advanced tools like iMotions Lab to deepen understanding of human behavior and experiences. Below, we explore several emerging trends and innovations that are shaping the future of MMR.

1. Integration of Biometric and Physiological Measures:

The incorporation of biometric data, such as eye tracking, facial expression analysis, heart rate variability, and galvanic skin response, into MMR designs is a growing trend. These measures provide objective, physiological insights that complement traditional qualitative and quantitative data, offering a more nuanced understanding of participants’ responses and behaviors. Tools like iMotions Lab facilitate the simultaneous collection and analysis of biometric data alongside survey, interview, or observational data, enriching the mixed methods approach.

2. Advanced Data Visualization Techniques:

With the increasing complexity of data in MMR, advanced visualization techniques are becoming crucial for interpreting and communicating findings. Interactive dashboards, data storytelling, and immersive visualizations (e.g., virtual reality) are being used to present integrated findings in more engaging and accessible ways. These techniques help researchers and stakeholders alike to navigate and make sense of complex datasets, highlighting relationships between quantitative metrics and qualitative insights.

3. Use of Big Data and Machine Learning:

The integration of big data analytics and machine learning algorithms into MMR represents an exciting frontier. These technologies can handle large volumes of data from diverse sources, including social media, wearable devices, and online interactions, offering new opportunities for mixed methods analyses. Machine learning can assist in identifying patterns and themes within qualitative data, while big data can enhance the breadth and depth of quantitative analyses, allowing for more dynamic and real-time research designs.

4. Participatory and Community-Based Approaches:

There is a growing emphasis on participatory research methods that actively involve participants or communities in the research process. These approaches are particularly relevant in MMR, where the combination of qualitative and quantitative methods can be designed to reflect the needs, perspectives, and voices of those being studied. Such participatory approaches can enhance the relevance and impact of research findings, promoting more equitable and inclusive research practices.

5. Focus on Methodological and Analytical Integration:

As MMR matures, there is an increasing focus on not just the combination but the deep integration of methodological approaches and analytical strategies. This involves developing new frameworks and techniques for truly integrating qualitative and quantitative data at the level of analysis, rather than merely combining disparate datasets. Researchers are exploring ways to intertwine narratives with numbers, embedding qualitative insights within quantitative models, and vice versa, to produce more coherent and impactful understandings.

6. Ethical and Reflexive Practices:

With the advancement of technologies and methodologies in MMR, ethical considerations and reflexive practices are becoming more central. Researchers are recognizing the importance of addressing ethical challenges that arise from new data collection methods, particularly biometric measures, and ensuring participants’ privacy, consent, and data security. Reflexivity—being aware of and critically reflecting on one’s influence on the research—becomes crucial as methodologies and technologies evolve.

These emerging trends and innovations signify a dynamic period of growth and diversification for Mixed Methods Research. By embracing new technologies like biometric research tools, advanced analytical techniques, and participatory approaches, MMR is poised to offer richer, more comprehensive insights into the complexities of human behavior and social phenomena.

Resources for Further Learning

The field of Mixed Methods Research (MMR) is rich with resources for researchers at all levels seeking to enhance their understanding and application of this approach. Below, we highlight key resources, including books, journals, professional organizations, and online platforms, that offer valuable insights into MMR. These resources can help researchers develop their skills, stay abreast of the latest developments, and connect with the MMR community.

“Designing and Conducting Mixed Methods Research” by John W. Creswell and Vicki L. Plano Clark: A foundational text that provides comprehensive coverage of mixed methods research design and implementation, suitable for beginners and experienced researchers alike.

“Mixed Methods Research for Nursing and the Health Sciences” by Sharon Andrew and Elizabeth J. Halcomb: This book focuses on the application of MMR in nursing and health sciences, offering practical examples and guidance for integrating quantitative and qualitative approaches.

“Sage Handbook of Mixed Methods in Social & Behavioral Research” by Abbas Tashakkori and Charles Teddlie: A comprehensive handbook that covers theoretical, methodological, and practical aspects of MMR, including advanced topics and emerging trends.

Journal of Mixed Methods Research (SAGE Publications): A leading journal dedicated to the development and dissemination of mixed methods research across social, behavioral, health, and human sciences. https://journals.sagepub.com/home/mmr

International Journal of Multiple Research Approaches: Publishes articles on the use of multiple research approaches, including mixed methods, providing insights into complex research questions and methodological innovations. https://ijmra.org/

Professional Organizations:

Mixed Methods International Research Association (MMIRA): An organization dedicated to promoting the development of MMR across disciplines. MMIRA offers conferences, webinars, and networking opportunities for members. https://mmira.wildapricot.org/

American Educational Research Association (AERA) Mixed Methods Research Special Interest Group (SIG): A SIG within AERA that focuses on the use of mixed methods in educational research, providing a forum for discussion, collaboration, and professional development. https://www.aera.net/SIG158/Mixed-Methods-Research-SIG-158

Online Platforms and Resources:

MethodSpace (SAGE): An online community for researchers interested in methodological discussions, including mixed methods. MethodSpace offers articles, webinars, and forums for researchers to connect and share ideas. https://researchmethodscommunity.sagepub.com/

iMotions Blog: Provides insights into the application of biometric research in mixed methods studies, including case studies, tutorials, and best practices for using biometric data in research.

Coursera and edX: These online learning platforms offer courses on mixed methods research and related topics, taught by experts from leading universities and institutions. Courses range from introductory to advanced levels, accommodating learners with different backgrounds and interests.

Mixed Methods Research (MMR) represents a dynamic and comprehensive approach to exploring complex research questions across a wide range of disciplines. By integrating quantitative and qualitative methods, MMR leverages the strengths of both to provide a more nuanced, complete understanding of research phenomena than either method could achieve alone. This approach allows researchers to explore the richness of human experiences, behaviors, and interactions with a depth and breadth unparalleled by traditional research methodologies.

The evolution of MMR reflects a growing recognition of the complexity of social, health, educational, and behavioral phenomena, necessitating research approaches that can capture this complexity in all its dimensions. The future of MMR is bright, with emerging trends and innovations, such as the integration of biometric data, advanced data visualization techniques, and the use of big data and machine learning, expanding its potential to uncover insights into human behavior and social phenomena.

Challenges remain, including methodological biases, resource constraints, and ethical considerations, but these are outweighed by the significant benefits MMR offers. The field is supported by a robust framework of guidelines for conducting and reporting MMR, alongside criteria for evaluating its quality, ensuring that studies are conducted with rigor and integrity.

In conclusion, Mixed Methods Research is an indispensable tool in the researcher’s arsenal, capable of addressing the multifaceted questions that define our increasingly complex world. Its capacity to integrate quantitative breadth with qualitative depth makes it uniquely suited to exploring the nuances of human behavior and social structures. As we look to the future, MMR holds the promise of fostering interdisciplinary collaboration, enhancing our understanding of complex phenomena, and ultimately contributing to the betterment of society through informed research and practice.

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http://orcid.org/0000-0003-4808-3880 Andrew Booth 1 , 2 ,
Isolde Sommer 3 , 4 ,
http://orcid.org/0000-0003-4238-5984 Jane Noyes 2 , 5 ,
Catherine Houghton 2 , 6 ,
Fiona Campbell 1 , 7
The Cochrane Rapid Reviews Methods Group and Cochrane Qualitative and Implementation Methods Group (CQIMG)
1 EnSyGN Sheffield Evidence Synthesis Group , University of Sheffield , Sheffield , UK
2 Cochrane Qualitative and Implementation Methods Group (CQIMG) , London , UK
3 Department for Evidence-based Medicine and Evaluation , University for Continuing Education Krems , Krems , Austria
4 Cochrane Rapid Reviews Group & Cochrane Austria , Krems , Austria
5 Bangor University , Bangor , UK
6 University of Galway , Galway , Ireland
7 University of Newcastle upon Tyne , Newcastle upon Tyne , UK
Correspondence to Professor Andrew Booth, Univ Sheffield, Sheffield, UK; a.booth{at}sheffield.ac.uk

This paper forms part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group and addresses rapid qualitative evidence syntheses (QESs), which use modified systematic, transparent and reproducible methodsu to accelerate the synthesis of qualitative evidence when faced with resource constraints. This guidance covers the review process as it relates to synthesis of qualitative research. ‘Rapid’ or ‘resource-constrained’ QES require use of templates and targeted knowledge user involvement. Clear definition of perspectives and decisions on indirect evidence, sampling and use of existing QES help in targeting eligibility criteria. Involvement of an information specialist, especially in prioritising databases, targeting grey literature and planning supplemental searches, can prove invaluable. Use of templates and frameworks in study selection and data extraction can be accompanied by quality assurance procedures targeting areas of likely weakness. Current Cochrane guidance informs selection of tools for quality assessment and of synthesis method. Thematic and framework synthesis facilitate efficient synthesis of large numbers of studies or plentiful data. Finally, judicious use of Grading of Recommendations Assessment, Development and Evaluation approach for assessing the Confidence of Evidence from Reviews of Qualitative research assessments and of software as appropriate help to achieve a timely and useful review product.

Systematic Reviews as Topic
Patient Care

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No data are available. Not applicable. All data is from published articles.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjebm-2023-112620

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Rapid Qualitative Evidence Synthesis (QES) is a relatively recent innovation in evidence synthesis and few published examples currently exists.

Guidance for authoring a rapid QES is scattered and requires compilation and summary.

WHAT THIS STUDY ADDS

This paper represents the first attempt to compile current guidance, illustrated by the experience of several international review teams.

We identify features of rapid QES methods that could be accelerated or abbreviated and where methods resemble those for conventional QESs.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This paper offers guidance for researchers when conducting a rapid QES and informs commissioners of research and policy-makers what to expect when commissioning such a review.

Introduction

This paper forms part of a series from the Cochrane Rapid Reviews Methods Group providing methodological guidance for rapid reviews. While other papers in the series 1–4 focus on generic considerations, we aim to provide in-depth recommendations specific to a resource-constrained (or rapid) qualitative evidence synthesis (rQES). 5 This paper is accompanied by recommended resources ( online supplemental appendix A ) and an elaboration with practical considerations ( online supplemental appendix B ).

Supplemental material

The role of qualitative evidence in decision-making is increasingly recognised. 6 This, in turn, has led to appreciation of the value of qualitative evidence syntheses (QESs) that summarise findings across multiple contexts. 7 Recognition of the need for such syntheses to be available at the time most useful to decision-making has, in turn, driven demand for rapid qualitative evidence syntheses. 8 The breadth of potential rQES mirrors the versatility of QES in general (from focused questions to broad overviews) and outputs range from descriptive thematic maps through to theory-informed syntheses (see table 1 ).

View inline

Glossary of important terms (alphabetically)

As with other resource-constrained reviews, no one size fits all. A team should start by specifying the phenomenon of interest, the review question, 9 the perspectives to be included 9 and the sample to be determined and selected. 10 Subsequently, the team must finalise the appropriate choice of synthesis. 11 Above all, the review team should consider the intended knowledge users, 3 including requirements of the funder.

An rQES team, in particular, cannot afford any extra time or resource requirements that might arise from either a misunderstanding of the review question, an unclear picture of user requirements or an inappropriate choice of methods. The team seeks to align the review question and the requirements of the knowledge user with available time and resources. They also need to ensure that the choice of data and choice of synthesis are appropriate to the intended ‘knowledge claims’ (epistemology) made by the rQES. 11 This involves the team asking ‘what types of data are meaningful for this review question?’, ‘what types of data are trustworthy?’ and ‘is the favoured synthesis method appropriate for this type of data?’. 12 This paper aims to help rQES teams to choose methods that best fit their project while understanding the limitations of those choices. Our recommendations derive from current QES guidance, 5 evidence on modified QES methods, 8 13 and practical experience. 14 15

This paper presents an overview of considerations and recommendations as described in table 2 . Supplemental materials including additional resources details of our recommendations and practical examples are provided in online supplemental appendices A and B .

Recommendations for resource-constrained qualitative evidence synthesis (rQES)

Setting the review question and topic refinement

Rapid reviews summarise information from multiple research studies to produce evidence for ‘the public, researchers, policymakers and funders in a systematic, resource-efficient manner’. 16 Involvement of knowledge users is critical. 3 Given time constraints, individual knowledge users could be asked only to feedback on very specific decisions and tasks or on selective sections of the protocol. Specifically, whenever a QES is abbreviated or accelerated, a team should ensure that the review question is agreed by a minimum number of knowledge users with expertise or experience that reflects all the important review perspectives and with authority to approve the final version 2 5 11 ( table 2 , item R1).

Involvement of topic experts can ensure that the rQES is responsive to need. 14 17 One Cochrane rQES saved considerable time by agreeing the review topic within a single meeting and one-phase iteration. 9 Decisions on topics to be omitted are also informed by a knowledge of existing QESs. 17

An information specialist can help to manage the quantity and quality of available evidence by setting conceptual boundaries and logistic limits. A structured question format, such as Setting-Perspective-Interest, phenomenon of-Comparison-Evaluation or Population-Interest, phenomenon of-Context helps in communicating the scope and, subsequently, in operationalising study selection. 9 18

Scoping (of review parameters) and mapping (of key types of evidence and likely richness of data) helps when planning the review. 5 19 The option to choose purposive sampling over comprehensive sampling approaches, as offered by standard QES, may be particularly helpful in the context of a rapid QES. 8 Once a team knows the approximate number and distribution of studies, perhaps mapping them against country, age, ethnicity, etc), they can decide whether or not to use purposive sampling. 12 An rQES for the WHO combined purposive with variation sampling. Sampling in two stages started by reducing the initial number of studies to a more manageable sampling frame and then sampling approximately a third of the remaining studies from within the sampling frame. 20

Sampling may target richer studies and/or privilege diversity. 8 21 A rich qualitative study typically illustrates findings with verbatim extracts from transcripts from interviews or textual responses from questionnaires. Rich studies are often found in specialist qualitative research or social science journals. In contrast, less rich studies may itemise themes with an occasional indicative text extract and tend to summarise findings. In clinical or biomedical journals less rich findings may be placed within a single table or box.

No rule exists on an optimal number of studies; too many studies makes it challenging to ‘maintain insight’, 22 too few does not sustain rigorous analysis. 23 Guidance on sampling is available from the forthcoming Cochrane-Campbell QES Handbook.

A review team can use templates to fast-track writing of a protocol. The protocol should always be publicly available ( table 2 , item R2). 24 25 Formal registration may require that the team has not commenced data extraction but should be considered if it does not compromise the rQES timeframe. Time pressures may require that methods are left suitably flexible to allow well-justified changes to be made as a detailed picture of the studies and data emerge. 26 The first Cochrane rQES drew heavily on text from a joint protocol/review template previously produced within Cochrane. 24

Setting eligibility criteria

An rQES team may need to limit the number of perspectives, focusing on those most important for decision-making 5 9 27 ( table 2 , item R3). Beyond the patients/clients each additional perspective (eg, family members, health professionals, other professionals, etc) multiplies the additional effort involved.

A rapid QES may require strict date and setting restrictions 17 and language restrictions that accommodate the specific requirements of the review. Specifically, the team should consider whether changes in context over time or substantive differences between geographical regions could be used to justify a narrower date range or a limited coverage of countries and/or languages. The team should also decide if ‘indirect evidence’ is to substitute for the absence of direct evidence. An rQES typically focuses on direct evidence, except when only indirect evidence is available 28 ( table 2 , item R4). Decisions on relevance are challenging—precautions for swine influenza may inform precautions for bird influenza. 28 A smoking ban may operate similarly to seat belt legislation, etc. A review team should identify where such shared mechanisms might operate. 28 An rQES team must also decide whether to use frameworks or models to focus the review. Theories may be unearthed within the topic search or be already known to team members, fro example, Theory of Planned Behaviour. 29

Options for managing the quantity and quality of studies and data emerge during the scoping (see above). In summary, the review team should consider privileging rich qualitative studies 2 ; consider a stepwise approach to inclusion of qualitative data and explore the possibility of sampling ( table 2 , item R5). For example, where data is plentiful an rQES may be limited to qualitative research and/or to mixed methods studies. Where data is less plentiful then surveys or other qualitative data sources may need to be included. Where plentiful reviews already exist, a team may decide to conduct a review of reviews 5 by including multiple QES within a mega-synthesis 28 29 ( table 2 , item R6).

Searching for QES merits its own guidance, 21–23 30 this section reinforces important considerations from guidance specific to qualitative research. Generic guidance for rapid reviews in this series broadly applies to rapid QESs. 1

In addition to journal articles, by far the most plentiful source, qualitative research is found in book chapters, theses and in published and unpublished reports. 21 Searches to support an rQES can (a) limit the number of databases searched, deliberately selecting databases from diverse disciplines, (b) use abbreviated study filters to retrieve qualitative designs and (c) employ high yield complementary methods (eg, reference checking, citation searching and Related Articles features). An information specialist (eg, librarian) should be involved in prioritising sources and search methods ( table 2 , item R7). 11 14

According to empirical evidence optimal database combinations include Scopus plus CINAHL or Scopus plus ProQuest Dissertations and Theses Global (two-database combinations) and Scopus plus CINAHL plus ProQuest Dissertations and Theses Global (three-database combination) with both choices retrieving between 89% and 92% of relevant studies. 30

If resources allow, searches should include one or two specialised databases ( table 2 , item R8) from different disciplines or contexts 21 (eg, social science databases, specialist discipline databases or regional or institutional repositories). Even when resources are limited, the information specialist should factor in time for peer review of at least one search strategy ( table 2 , item R9). 31 Searches for ‘grey literature’ should selectively target appropriate types of grey literature (such as theses or process evaluations) and supplemental searches, including citation chaining or Related Articles features ( table 2 , item R10). 32 The first Cochrane rQES reported that searching reference lists of key papers yielded an extra 30 candidate papers for review. However, the team documented exclusion of grey literature as a limitation of their review. 15

Study selection

Consistency in study selection is achieved by using templates, by gaining a shared team understanding of the audience and purpose, and by ongoing communication within, and beyond, the team. 2 33 Individuals may work in parallel on the same task, as in the first Cochrane rQES, or follow a ‘segmented’ approach where each reviewer is allocated a different task. 14 The use of machine learning in the specific context of rQES remains experimental. However, the possibility of developing qualitative study classifiers comparable to those for randomised controlled trials offers an achievable aspiration. 34

Title and abstract screening

The entire screening team should use pre-prepared, pretested title and abstract templates to limit the scale of piloting, calibration and testing ( table 2 , item R11). 1 14 The first Cochrane rQES team double-screened titles and abstracts within Covidence review software. 14 Disagreements were resolved with reference to a third reviewer achieving a shared understanding of the eligibility criteria and enhancing familiarity with target studies and insight from data. 14 The team should target and prioritise identified risks of either over-zealous inclusion or over-exclusion specific to each rQES ( table 2 , item R12). 14 The team should maximise opportunities to capture divergent views and perspectives within study findings. 35

Full-text screening

Full-text screening similarly benefits from using a pre-prepared pretested standardised template where possible 1 14 ( table 2 , item R11). If a single reviewer undertakes full-text screening, 8 the team should identify likely risks to trustworthiness of findings and focus quality control procedures (eg, use of additional reviewers and percentages for double screening) on specific threats 14 ( table 2 , item R13). The Cochrane rQES team opted for double screening to assist their immersion within the topic. 14

Data extraction

Data extraction of descriptive/contextual data may be facilitated by review management software (eg, EPPI-Reviewer) or home-made approaches using Google Forms, or other survey software. 36 Where extraction of qualitative findings requires line-by-line coding with multiple iterations of the data then a qualitative data management analysis package, such as QSR NVivo, reaps dividends. 36 The team must decide if, collectively, they favour extracting data to a template or coding direct within an electronic version of an article.

Quality control must be fit for purpose but not excessive. Published examples typically use a single reviewer for data extraction 8 with use of two independent reviewers being the exception. The team could limit data extraction to minimal essential items. They may also consider re-using descriptive details and findings previously extracted within previous well-conducted QES ( table 2 , item R14). A pre-existing framework, where readily identified, may help to structure the data extraction template. 15 37 The same framework may be used to present the findings. Some organisations may specify a preferred framework, such as an evidence-to-decision-making framework. 38

Assessment of methodological limitations

The QES community assess ‘methodological limitations’ rather than use ‘risk of bias’ terminology. An rQES team should pick an approach appropriate to their specific review. For example, a thematic map may not require assessment of individual studies—a brief statement of the generic limitations of the set of studies may be sufficient. However, for any synthesis that underpins practice recommendations 39 assessment of included studies is integral to the credibility of findings. In any decision-making context that involves recommendations or guidelines, an assessment of methodological limitations is mandatory. 40 41

Each review team should work with knowledge users to determine a review-specific approach to quality assessment. 27 While ‘traffic lights’, similar to the outputs from the Cochrane Risk of Bias tool, may facilitate rapid interpretation, accompanying textual notes are invaluable in highlighting specific areas for concern. In particular, the rQES team should demonstrate that they are aware (a) that research designs for qualitative research seek to elicit divergent views, rather than control for variation; (b) that, for qualitative research, the selection of the sample is far more informative than the size of the sample; and (c) that researchers from primary research, and equally reviewers for the qualitative synthesis, need to be thoughtful and reflexive about their possible influences on interpretation of either the primary data or the synthesised findings.

Selection of checklist

Numerous scales and checklists exist for assessing the quality of qualitative studies. In the absence of validated risk of bias tools for qualitative studies, the team should choose a tool according to Cochrane Qualitative and Implementation Methods Group (CQIMG) guidance together with expediency (according to ease of use, prior familiarity, etc) ( table 2 , item R15). 41 In comparison to the Critical Appraisal Skills Programme checklist which was never designed for use in synthesis, 42 the Cochrane qualitative tool is similarly easy to use and was designed for QES use. Work is underway to identify an assessment process that is compatible with QESs that support decision-making. 41 For now the choice of a checklist remains determined by interim Cochrane guidance and, beyond this, by personal preference and experience. For an rQES a team could use a single reviewer to assess methodological limitations, with verification of judgements (and support statements) by a second reviewer ( table 2 , item R16).

The CQIMG endorses three types of synthesis; thematic synthesis, framework synthesis and meta-ethnography ( box 1 ). 43 44 Rapid QES favour descriptive thematic synthesis 45 or framework synthesis, 46 47 except when theory generation (meta-ethnography 48 49 or analytical thematic synthesis) is a priority ( table 2 , item R17).

Choosing a method for rapid qualitative synthesis

Thematic synthesis: first choice method for rQES. 45 For example, in their rapid QES Crooks and colleagues 44 used a thematic synthesis to understand the experiences of both academic and lived experience coresearchers within palliative and end of life research. 45

Framework synthesis: alternative where a suitable framework can be speedily identified. 46 For example, Bright and colleagues 46 considered ‘best-fit framework synthesis’ as appropriate for mapping study findings to an ‘a priori framework of dimensions measured by prenatal maternal anxiety tools’ within their ‘streamlined and time-limited evidence review’. 47

Less commonly, an adapted meta-ethnographical approach was used for an implementation model of social distancing where supportive data (29 studies) was plentiful. 48 However, this QES demonstrates several features that subsequently challenge its original identification as ‘rapid’. 49

Abbrevations: QES, qualitative evidence synthesis; rQES, resource-constrained qualitative evidence synthesis.

The team should consider whether a conceptual model, theory or framework offers a rapid way for organising, coding, interpreting and presenting findings ( table 2 , item R18). If the extracted data appears rich enough to sustain further interpretation, data from a thematic or framework synthesis can subsequently be explored within a subsequent meta-ethnography. 43 However, this requires a team with substantial interpretative expertise. 11

Assessments of confidence in the evidence 4 are central to any rQES that seeks to support decision-making and the QES-specific Grading of Recommendations Assessment, Development and Evaluation approach for assessing the Confidence of Evidence from Reviews of Qualitative research (GRADE-CERQual) approach is designed to assess confidence in qualitative evidence. 50 This can be performed by a single reviewer, confirmed by a second reviewer. 26 Additional reviewers could verify all, or a sample of, assessments. For a rapid assessment a team must prioritise findings, using objective criteria; a WHO rQES focused only on the three ‘highly synthesised findings’. 20 The team could consider reusing GRADE-CERQual assessments from published QESs if findings are relevant and of demonstrable high quality ( table 2 , item R19). 50 No rapid approach to full application of GRADE-CERQual currently exists.

Reporting and record management

Little is written on optimal use of technology. 8 A rapid review is not a good time to learn review management software or qualitative analysis management software. Using such software for all general QES processes ( table 2 , item R20), and then harnessing these skills and tools when specifically under resource pressures, is a sounder strategy. Good file labelling and folder management and a ‘develop once, re-use multi-times’ approach facilitates resource savings.

Reporting requirements include the meta-ethnography reporting guidance (eMERGe) 51 and the Enhancing transparency in reporting the synthesis of qualitative research (ENTREQ) statement. 52 An rQES should describe limitations and their implications for confidence in the evidence even more thoroughly than a regular QES; detailing the consequences of fast-tracking, streamlining or of omitting processes all together. 8 Time spent documenting reflexivity is similarly important. 27 If QES methodology is to remain credible rapid approaches must be applied with insight and documented with circumspection. 53 54 (56)

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1

Correction notice Since this paper first published, updates have been made to the left hand column of table 2.

Contributors All authors (AB, IS, JN, CH, FC) have made substantial contributions to the conception and design of the guidance document. AB led on drafting the work and revising it critically for important intellectual content. All other authors (IS, JN, CH, FC) contributed to revisions of the document. All authors (AB, IS, JN, CH, FC) have given final approval of the version to be published. As members of the Cochrane Qualitative and Implementation Methods Group and/or the Cochrane Rapid Reviews Methods Group all authors (AB, IS, JN, CH, FC) agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests AB is co-convenor of the Cochrane Qualitative and Implementation Methods Group. In the last 36 months, he received royalties from Systematic Approaches To a Successful Literature Review (Sage 3rd edition), honoraria from the Agency for Healthcare Research and Quality, and travel support from the WHO. JN is lead convenor of the Cochrane Qualitative and Implementation Methods Group. In the last 36 months, she has received honoraria from the Agency for Healthcare Research and Quality and travel support from the WHO. CH is co-convenor of the Cochrane Qualitative and Implementation Methods Group.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; internally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being "qualitative," the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term "qualitative." Then, drawing on ideas we find scattered ...
Introduction to qualitative research methods
INTRODUCTION. Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures.
Qualitative Research Definition and Methods
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 ...
Qualitative Study
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 intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants ...
Quantitative and 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.
PDF Qualitative Research
qualitative methods to address questions about people's ways of organizing, relating to, and interacting with the world. Despite the interdisciplinary recognition of the ... We begin this chapter by providing a definition of qualitative research that serves to frame the content and scope of the chapters that follow. We then provide a brief
Planning Qualitative Research: Design and Decision Making for New
While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...
What is Qualitative in Research
Our definition demarcates qualitative research from non-qualitative by spelling out the qualitative elements of research, which advances a criterion of evaluation. In addition, there is definitely research that meets the characteristics of being qualitative, but that is uninteresting, irrelevant, or essentially useless (see Alvesson et al. 2017 ...
Qualitative Study
Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data.
What is Qualitative Research? Methods and Examples
Qualitative research seeks to gain insights and understand people's experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people's beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or ...
Qualitative vs Quantitative Research: What's the Difference?
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.
How to use and assess qualitative research methods
Abstract. This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions ...
Qualitative Research: Definition, Methods and Examples
Qualitative research definition: Qualitative research is a type of research methodology that aims to understand and interpret social phenomena by exploring the subjective experiences, perspectives, and meanings of individuals or groups. It focuses on gathering in-depth, non-numerical data through interviews, observations, and analysis of texts ...
What is Qualitative in Qualitative Research
A fourth issue is that the "implicit use of methods in qualitative research makes the field far less standardized than the ... defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables ...
Mixed Methods Research
Challenge: Conducting MMR requires expertise in both quantitative and qualitative research methods, ... This article offers a comprehensive definition of mixed methods research, discussing its foundations, applications, and implications for social sciences research. It has become a key reference point for scholars engaging in MMR.
Rapid reviews methods series: guidance on rapid qualitative evidence
This paper forms part of a series of methodological guidance from the Cochrane Rapid Reviews Methods Group and addresses rapid qualitative evidence syntheses (QESs), which use modified systematic, transparent and reproducible methodsu to accelerate the synthesis of qualitative evidence when faced with resource constraints. This guidance covers the review process as it relates to synthesis of ...
Full article: COVID-19 vaccination perspectives among patients with
Citation 10-12 Using qualitative and quantitative methods, ... Citation 10 Our qualitative findings also supplement prior research utilizing quantitative methods to assess differences in fear and vaccine hesitancy among groups who have not experienced a COVID-19 infection, who have experienced a COVID-19 infection but did not develop Long ...

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What Is Qualitative Research? | Methods & Examples

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Qualitative Research : Definition

Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research

Qualitative Research Methods

One-to-One Interview

Focus Groups

Ethnographic Studies

Text Analysis

Process of Observation

Record Keeping

Qualitative Research Analysis Methods

Thematic Analysis

Content Analysis

Discourse Analysis

Grounded Theory Analysis

Narrative Analysis

Phenomenological Analysis

Comparative Analysis

Applications of Qualitative Research

How to Conduct Qualitative Research

Examples of Qualitative Research

Purpose of Qualitative Research

When to use Qualitative Research

Characteristics of Qualitative Research

Advantages of Qualitative Research

Limitations of Qualitative Research

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Muhammad Hassan

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1. One-on-one interview

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The SAGE Encyclopedia of Qualitative Research Methods

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