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Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

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

Types of Qualitative Research

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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Research Paper Guide

Types Of Qualitative Research

Nova A.

8 Types of Qualitative Research - Overview & Examples

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types of qualitative research

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How to Write a Research Methodology for a Research Paper

Are you overwhelmed by the multitude of qualitative research methods available? It's no secret that choosing the right approach can leave you stuck at the starting line of your research.

Selecting an unsuitable method can lead to wasted time, resources, and potentially skewed results. But with so many options to consider, it's easy to feel lost in the complexities of qualitative research.

In this comprehensive guide, we will explain the types of qualitative research, their unique characteristics, advantages, and best use cases for each method.

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  • 1. What is Qualitative Research?
  • 2. Types of Qualitative Research Methods
  • 3. Types of Data Analysis in Qualitative Research 

What is Qualitative Research?

Qualitative research is a robust and flexible methodology used to explore and understand complex phenomena in-depth. 

Unlike quantitative research , qualitative research dives into the rich and complex aspects of human experiences, behaviors, and perceptions.

At its core, this type of research question seek to answer for:

  • Why do people think or behave a certain way?
  • What are the underlying motivations and meanings behind actions?
  • How do individuals perceive and interpret the world around them?

This approach values context, diversity, and the unique perspectives of participants. 

Rather than seeking generalizable findings applicable to a broad population, qualitative research aims for detailed insights, patterns, and themes that come from the people being studied.

Characteristics of Qualitative Research 

Qualitative research possesses the following characteristics: 

  • Subjective Perspective: Qualitative research explores subjective experiences, emphasizing the uniqueness of human behavior and opinions.
  • In-Depth Exploration: It involves deep investigation, allowing a comprehensive understanding of specific phenomena.
  • Open-Ended Questions: Qualitative research uses open-ended questions to encourage detailed, descriptive responses.
  • Contextual Understanding: It emphasizes the importance of understanding the research context and setting.
  • Rich Descriptions: Qualitative research produces rich, descriptive findings that contribute to a nuanced understanding of the topic.

Types of Qualitative Research Methods

Researchers collect data on the targeted population, place, or event by using different types of qualitative research analysis.

Each qualitative research method offers a distinct perspective, enabling researchers to reveal concealed meanings, patterns, and valuable insights.

Below are the most commonly used qualitative research types for writing a paper.

Ethnographic Research Method 

Ethnography, a subfield of anthropology, provides a scientific approach to examining human societies and cultures. It ranks among the most widely employed qualitative research techniques.

In ethnographic field notes, researchers actively engage with the environment and live alongside the focus group. 

This immersive interaction allows researchers to gain insights into the objectives, motivations, challenges, and distinctive cultural attributes of the individuals under study.

Key cultural characteristics that ethnography helps to illustrate encompass:

  • Geographical Location
  • Religious Practices
  • Tribal Systems
  • Shared Experiences

Unlike traditional survey and interview-based research methods, ethnographers don't rely on structured questioning. 

Instead, they become observers within the community, emphasizing participant observation over an extended period. However, it may also be appropriate to complement observations with interviews of individuals who possess knowledge of the culture.

Ethnographic research can present challenges if the researcher is unfamiliar with the social norms and language of the group being studied. 

Furthermore, interpretations made by outsiders may lead to misinterpretations or confusion. Therefore, thorough validation of data is essential before presenting findings.

Narrative Method 

The narrative research design unfolds over an extended period to compile data, much like crafting a cohesive story. Similar to a narrative structure, it begins with a starting point and progresses through various life situations.

In this method, researchers engage in in-depth interviews and review relevant documents. They explore events that have had a significant impact on an individual's personality and life journey. Interviews may occur over weeks, months, or even years, depending on the depth and scope of the narrative being studied.

The outcome of narrative research is the presentation of a concise story that captures essential themes, conflicts, and challenges. It provides a holistic view of the individual's experiences, both positive and negative, which have shaped their unique narrative.

Phenomenological Method 

The term "phenomenological" pertains to the study of phenomena, which can encompass events, situations, or experiences. 

This method is ideal for examining a subject from multiple perspectives and contributing to existing knowledge, with a particular focus on subjective experiences.

Researchers employing the phenomenological method use various data collection techniques, including interviews, site visits, observations, surveys, and document reviews. 

These methods help gather rich and diverse data about the phenomenon under investigation.

A central aspect of this technique is capturing how participants experience events or activities, delving into their subjective viewpoints. Ultimately, the research results in the creation of a thematic database that validates the findings and offers insights from the subject's perspective.

Grounded Theory Method

A grounded theory approach differs from a phenomenological study in that it seeks to explain, provide reasons for, or develop theories behind an event or phenomenon. 

It serves as a means to construct new theories by systematically collecting and analyzing data related to a specific phenomenon.

Researchers employing the grounded theory method utilize a variety of data collection techniques, including observation, interviews, literature review , and the analysis of relevant documents. 

The focus of content analysis is not individual behaviors but a specific phenomenon or incident.

This method typically involves various coding techniques and large sample sizes to identify themes and develop more comprehensive theories.

Case Study Research 

The case study approach entails a comprehensive examination of a subject over an extended period, with a focus on providing detailed insights into the subject, which can be an event, person, business, or place.

Data for case studies is collected from diverse sources, including interviews, direct observation, historical records, and documentation.

Case studies find applications across various disciplines, including law, education, medicine, and the sciences. They can serve both descriptive and explanatory purposes, making them a versatile research methodology .

Researchers often turn to the case study method when they want to explore:

  • 'How' and 'why' research questions
  • Behaviors under observation
  • Understanding a specific phenomenon
  • The contextual factors influencing the phenomena

Historical Method

The historical method aims to describe and analyze past events, offering insights into present patterns and the potential to predict future scenarios. 

Researchers formulate research questions based on a hypothetical idea and then rigorously test this idea using multiple historical resources.

Key steps in the historical method include:

  • Developing a research idea
  • Identifying appropriate sources such as archives and libraries
  • Ensuring the reliability and validity of these sources
  • Creating a well-organized research outline
  • Systematically collecting research data

The analysis phase involves critically assessing the collected data, accepting or rejecting it based on credibility, and identifying any conflicting evidence.

Ultimately, the outcomes of the historical method are presented in the form of a biography or a scholarly paper that provides a comprehensive account of the research findings.

Action Research 

Action research is a dynamic research approach focused on addressing practical challenges in real-world settings while simultaneously conducting research to improve the situation. 

It follows a cyclic process, starting with the identification of a specific issue or problem in a particular context.

The key steps in action research include:

  • Planning and implementing actions to address the issue
  • Collecting data during the action phase to understand its impact
  • Reflecting on the data and analyzing it to gain insights
  • Adjusting the action plan based on the analysis

This process may be iterative, with multiple cycles of action and reflection.

The outcomes of action research are practical solutions and improved practices that directly benefit the context in which the research is conducted. Additionally, it leads to a deeper and more nuanced understanding of the issue under investigation.

Focus Groups 

Focus groups are a qualitative research method used to gather in-depth insights and perspectives on a specific topic or research question. 

This approach involves assembling a small group of participants who possess relevant knowledge or experiences related to the research focus.

Key steps in the focus group method include:

  • Selecting participants
  • Moderating the discussion
  • Structuring the conversation around open-ended questions
  • Collecting data through audio or video recordings and note-taking 

The discussion is dynamic and interactive, encouraging participants to share their thoughts, experiences, and opinions.

The analysis phase involves reviewing the data collected from the focus group discussion to identify common themes, patterns, and valuable insights. Focus groups provide rich qualitative data that offer a deeper and more nuanced understanding of the research topic or question.

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Types of Data Analysis in Qualitative Research 

Qualitative research employs different data analysis methods, each suited to specific research goals:

  • Thematic Analysis: Identifies recurring themes or concepts within data.
  • Content Analysis: Systematically categorizes and quantifies text or media content.
  • Narrative Analysis: Focuses on storytelling and narrative elements in data.
  • Grounded Theory Analysis: Develops or refines theories based on data.
  • Discourse Analysis: Examines language and communication patterns.
  • Framework Analysis: Organizes data using predefined categories.
  • Visual Analysis: Interprets visual data like photos or videos.
  • Cross-case Analysis: Compares patterns across multiple cases.

The choice depends on research questions and data type, enhancing understanding and insights.

Benefits of Qualitative Research 

Qualitative research offers valuable advantages, including:

  • Flexibility: Adaptable to various research questions and settings.
  • Holistic Approach: Explores multiple dimensions of phenomena.
  • Theory Development: Contributes to theory creation or refinement.
  • Participant Engagement: Fosters active participant involvement.
  • Complements Quantitative Research: Provides a comprehensive understanding.

All in all, different types of qualitative research methodology can assist in understanding the behavior and motivations of people. Similarly, it will also help in generating original ideas and formulating a better research problem.

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Nova A.

Nova Allison is a Digital Content Strategist with over eight years of experience. Nova has also worked as a technical and scientific writer. She is majorly involved in developing and reviewing online content plans that engage and resonate with audiences. Nova has a passion for writing that engages and informs her readers.

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Home Market Research

Qualitative Research Methods: Types, Analysis + Examples

Qualitative Research

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.

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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 :

Types of Qualitative Research

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

Characteristics of qualitative research methods - Infographics| QuestionPro

  • 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?

Table of contents

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

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

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

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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 analysing the data.

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

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

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

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

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

  • Flexibility

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

  • Natural settings

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

  • Meaningful insights

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

  • Generation of new ideas

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

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

  • Unreliability

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

  • Subjectivity

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

  • Limited generalisability

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

  • Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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9.4 Types of qualitative research designs

Learning objectives.

  • Define focus groups and outline how they differ from one-on-one interviews
  • Describe how to determine the best size for focus groups
  • Identify the important considerations in focus group composition
  • Discuss how to moderate focus groups
  • Identify the strengths and weaknesses of focus group methodology
  • Describe case study research, ethnography, and phenomenology.

There are various types of approaches to qualitative research.  This chapter presents information about focus groups, which are often used in social work research.  It also introduces case studies, ethnography, and phenomenology.

Focus Groups

Focus groups resemble qualitative interviews in that a researcher may prepare a guide in advance and interact with participants by asking them questions. But anyone who has conducted both one-on-one interviews and focus groups knows that each is unique. In an interview, usually one member (the research participant) is most active while the other (the researcher) plays the role of listener, conversation guider, and question-asker. Focus groups , on the other hand, are planned discussions designed to elicit group interaction and “obtain perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger & Casey, 2000, p. 5).  In focus groups, the researcher play a different role than in a one-on-one interview. The researcher’s aim is to get participants talking to each other,  to observe interactions among participants, and moderate the discussion.

qualitative research 4 types

There are numerous examples of focus group research. In their 2008 study, for example, Amy Slater and Marika Tiggemann (2010) conducted six focus groups with 49 adolescent girls between the ages of 13 and 15 to learn more about girls’ attitudes towards’ participation in sports. In order to get focus group participants to speak with one another rather than with the group facilitator, the focus group interview guide contained just two questions: “Can you tell me some of the reasons that girls stop playing sports or other physical activities?” and “Why do you think girls don’t play as much sport/physical activity as boys?” In another focus group study, Virpi Ylanne and Angie Williams (2009) held nine focus group sessions with adults of different ages to gauge their perceptions of how older characters are represented in television commercials. Among other considerations, the researchers were interested in discovering how focus group participants position themselves and others in terms of age stereotypes and identities during the group discussion. In both examples, the researchers’ core interest in group interaction could not have been assessed had interviews been conducted on a one-on-one basis, making the focus group method an ideal choice.

Who should be in your focus group?

In some ways, focus groups require more planning than other qualitative methods of data collection, such as one-on-one interviews in which a researcher may be better able to the dialogue. Researchers must take care to form focus groups with members who will want to interact with one another and to control the timing of the event so that participants are not asked nor expected to stay for a longer time than they’ve agreed to participate. The researcher should also be prepared to inform focus group participants of their responsibility to maintain the confidentiality of what is said in the group. But while the researcher can and should encourage all focus group members to maintain confidentiality, she should also clarify to participants that the unique nature of the group setting prevents her from being able to promise that confidentiality will be maintained by other participants. Once focus group members leave the research setting, researchers cannot control what they say to other people.

qualitative research 4 types

Group size should be determined in part by the topic of the interview and your sense of the likelihood that participants will have much to say without much prompting. If the topic is one about which you think participants feel passionately and will have much to say, a group of 3–5 could make sense. Groups larger than that, especially for heated topics, can easily become unmanageable. Some researchers say that a group of about 6–10 participants is the ideal size for focus group research (Morgan, 1997); others recommend that groups should include 3–12 participants (Adler & Clark, 2008).  The size of the focus group is ultimately the decision of the researcher. When forming groups and deciding how large or small to make them, take into consideration what you know about the topic and participants’ potential interest in, passion for, and feelings about the topic. Also consider your comfort level and experience in conducting focus groups. These factors will help you decide which size is right in your particular case.

It may seem counterintuitive, but in general, it is better to form focus groups consisting of participants who do not know one another than to create groups consisting of friends, relatives, or acquaintances (Agar & MacDonald, 1995).  The reason is that group members who know each other may not share some taken-for-granted knowledge or assumptions. In research, it is precisely the  taken-for-granted knowledge that is often of interest; thus, the focus group researcher should avoid setting up interactions where participants may be discouraged to question or raise issues that they take for granted. However, group members should not be so different from one another that participants will be unlikely to feel comfortable talking with one another.

Focus group researchers must carefully consider the composition of the groups they put together. In his text on conducting focus groups, Morgan (1997) suggests that “homogeneity in background and not homogeneity in attitudes” (p. 36) should be the goal, since participants must feel comfortable speaking up but must also have enough differences to facilitate a productive discussion.  Whatever composition a researcher designs for her focus groups, the important point to keep in mind is that focus group dynamics are shaped by multiple social contexts (Hollander, 2004). Participants’ silences as well as their speech may be shaped by gender, race, class, sexuality, age, or other background characteristics or social dynamics—all of which might be suppressed or exacerbated depending on the composition of the group. Hollander (2004) suggests that researchers must pay careful attention to group composition, must be attentive to group dynamics during the focus group discussion, and should use multiple methods of data collection in order to “untangle participants’ responses and their relationship to the social contexts of the focus group” (p. 632).

The role of the moderator

In addition to the importance of group composition, focus groups also require skillful moderation. A moderator is the researcher tasked with facilitating the conversation in the focus group. Participants may ask each other follow-up questions, agree or disagree with one another, display body language that tells us something about their feelings about the conversation, or even come up with questions not previously conceived of by the researcher. It is just these sorts of interactions and displays that are of interest to the researcher. A researcher conducting focus groups collects data on more than people’s direct responses to her question, as in interviews.

The moderator’s job is not to ask questions to each person individually, but to stimulate conversation between participants. It is important to set ground rules for focus groups at the outset of the discussion. Remind participants you’ve invited them to participate because you want to hear from all of them. Therefore, the group should aim to let just one person speak at a time and avoid letting just a couple of participants dominate the conversation. One way to do this is to begin the discussion by asking participants to briefly introduce themselves or to provide a brief response to an opening question. This will help set the tone of having all group members participate. Also, ask participants to avoid having side conversations; thoughts or reactions to what is said in the group are important and should be shared with everyone.

As the focus group gets rolling, the moderator will play a less active role as participants talk to one another. There may be times when the conversation stagnates or when you, as moderator, wish to guide the conversation in another direction. In these instances, it is important to demonstrate that you’ve been paying attention to what participants have said. Being prepared to interject statements or questions such as “I’d really like to hear more about what Sunil and Joe think about what Dominick and Jae have been saying” or “Several of you have mentioned X. What do others think about this?” will be important for keeping the conversation going. It can also help redirect the conversation, shift the focus to participants who have been less active in the group, and serve as a cue to those who may be dominating the conversation that it is time to allow others to speak. Researchers may choose to use multiple moderators to make managing these various tasks easier.

Moderators are often too busy working with participants to take diligent notes during a focus group. It is helpful to have a note-taker who can record participants’ responses (Liamputtong, 2011). The note-taker creates, in essence, the first draft of interpretation for the data in the study. They note themes in responses, nonverbal cues, and other information to be included in the analysis later on. Focus groups are analyzed in a similar way as interviews; however, the interactive dimension between participants adds another element to the analytical process. Researchers must attend to the group dynamics of each focus group, as “verbal and nonverbal expressions, the tactical use of humour, interruptions in interaction, and disagreement between participants” are all data that are vital to include in analysis (Liamputtong, 2011, p. 175). Note-takers record these elements in field notes, which allows moderators to focus on the conversation.

Strengths and weaknesses of focus groups

Focus groups share many of the strengths and weaknesses of one-on-one qualitative interviews. Both methods can yield very detailed, in-depth information; are excellent for studying social processes; and provide researchers with an opportunity not only to hear what participants say but also to observe what they do in terms of their body language. Focus groups offer the added benefit of giving researchers a chance to collect data on human interaction by observing how group participants respond and react to one another. Like one-on-one qualitative interviews, focus groups can also be quite expensive and time-consuming. However, there may be some savings with focus groups as it takes fewer group events than one-on-one interviews to gather data from the same number of people. Another potential drawback of focus groups, which is not a concern for one-on-one interviews, is that one or two participants might dominate the group, silencing other participants. Careful planning and skillful moderation on the part of the researcher are crucial for avoiding, or at least dealing with, such possibilities. The various strengths and weaknesses of focus group research are summarized in Table 91.

Grounded Theory

Grounded theory has been widely used since its development in the late 1960s (Glaser & Strauss, 1967). Largely derived from schools of sociology, grounded theory involves emersion of the researcher in the field and in the data. Researchers follow a systematic set of procedures and a simultaneous approach to data collection and analysis. Grounded theory is most often used to generate rich explanations of complex actions, processes, and transitions. The primary mode of data collection is one-on-one participant interviews. Sample sizes tend to range from 20 to 30 individuals, sampled purposively (Padgett, 2016). However, sample sizes can be larger or smaller, depending on data saturation. Data saturation is the point in the qualitative research data collection process when no new information is being discovered. Researchers use a constant comparative approach in which previously collected data are analyzed during the same time frame as new data are being collected.  This allows the researchers to determine when new information is no longer being gleaned from data collection and analysis — that data saturation has been reached — in order to conclude the data collection phase.

Rather than apply or test existing grand theories, or “Big T” theories, grounded theory focuses on “small t” theories (Padgett, 2016). Grand theories, or “Big T” theories, are systems of principles, ideas, and concepts used to predict phenomena. These theories are backed up by facts and tested hypotheses. “Small t” theories are speculative and contingent upon specific contexts. In grounded theory, these “small t” theories are grounded in events and experiences and emerge from the analysis of the data collected.

One notable application of grounded theory produced a “small t” theory of acceptance following cancer diagnoses (Jakobsson, Horvath, & Ahlberg, 2005). Using grounded theory, the researchers interviewed nine patients in western Sweden. Data collection and analysis stopped when saturation was reached. The researchers found that action and knowledge, given with respect and continuity led to confidence which led to acceptance. This “small t” theory continues to be applied and further explored in other contexts.

Case study research

Case study research is an intensive longitudinal study of a phenomenon at one or more research sites for the purpose of deriving detailed, contextualized inferences and understanding the dynamic process underlying a phenomenon of interest. Case research is a unique research design in that it can be used in an interpretive manner to build theories or in a positivist manner to test theories. The previous chapter on case research discusses both techniques in depth and provides illustrative exemplars. Furthermore, the case researcher is a neutral observer (direct observation) in the social setting rather than an active participant (participant observation). As with any other interpretive approach, drawing meaningful inferences from case research depends heavily on the observational skills and integrative abilities of the researcher.


The ethnographic research method, derived largely from the field of anthropology, emphasizes studying a phenomenon within the context of its culture. The researcher must be deeply immersed in the social culture over an extended period of time (usually 8 months to 2 years) and should engage, observe, and record the daily life of the studied culture and its social participants within their natural setting. The primary mode of data collection is participant observation, and data analysis involves a “sense-making” approach. In addition, the researcher must take extensive field notes, and narrate her experience in descriptive detail so that readers may experience the same culture as the researcher. In this method, the researcher has two roles: rely on her unique knowledge and engagement to generate insights (theory), and convince the scientific community of the trans-situational nature of the studied phenomenon.

The classic example of ethnographic research is Jane Goodall’s study of primate behaviors, where she lived with chimpanzees in their natural habitat at Gombe National Park in Tanzania, observed their behaviors, interacted with them, and shared their lives. During that process, she learnt and chronicled how chimpanzees seek food and shelter, how they socialize with each other, their communication patterns, their mating behaviors, and so forth. A more contemporary example of ethnographic research is Myra Bluebond-Langer’s (1996)14 study of decision making in families with children suffering from life-threatening illnesses, and the physical, psychological, environmental, ethical, legal, and cultural issues that influence such decision-making. The researcher followed the experiences of approximately 80 children with incurable illnesses and their families for a period of over two years. Data collection involved participant observation and formal/informal conversations with children, their parents and relatives, and health care providers to document their lived experience.


Phenomenology is a research method that emphasizes the study of conscious experiences as a way of understanding the reality around us. Phenomenology is concerned with the systematic reflection and analysis of phenomena associated with conscious experiences, such as human judgment, perceptions, and actions, with the goal of (1) appreciating and describing social reality from the diverse subjective perspectives of the participants involved, and (2) understanding the symbolic meanings (“deep structure”) underlying these subjective experiences. Phenomenological inquiry requires that researchers eliminate any prior assumptions and personal biases, empathize with the participant’s situation, and tune into existential dimensions of that situation, so that they can fully understand the deep structures that drives the conscious thinking, feeling, and behavior of the studied participants.

Some researchers view phenomenology as a philosophy rather than as a research method. In response to this criticism, Giorgi and Giorgi (2003) developed an existential phenomenological research method to guide studies in this area. This method can be grouped into data collection and data analysis phases. In the data collection phase, participants embedded in a social phenomenon are interviewed to capture their subjective experiences and perspectives regarding the phenomenon under investigation. Examples of questions that may be asked include “can you describe a typical day” or “can you describe that particular incident in more detail?” These interviews are recorded and transcribed for further analysis. During data analysis, the researcher reads the transcripts to: (1) get a sense of the whole, and (2) establish “units of significance” that can faithfully represent participants’ subjective experiences. Examples of such units of significance are concepts such as “felt space” and “felt time,” which are then used to document participants’ psychological experiences. For instance, did participants feel safe, free, trapped, or joyous when experiencing a phenomenon (“felt-space”)? Did they feel that their experience was pressured, slow, or discontinuous (“felt-time”)? Phenomenological analysis should take into account the participants’ temporal landscape (i.e., their sense of past, present, and future), and the researcher must transpose herself in an imaginary sense in the participant’s situation (i.e., temporarily live the participant’s life). The participants’ lived experience is described in form of a narrative or using emergent themes. The analysis then delves into these themes to identify multiple layers of meaning while retaining the fragility and ambiguity of subjects’ lived experiences.

Key Takeaways

  • In terms of focus group composition, homogeneity of background among participants is recommended while diverse attitudes within the group are ideal.
  • The goal of a focus group is to get participants to talk with one another rather than the researcher.
  • Like one-on-one qualitative interviews, focus groups can yield very detailed information, are excellent for studying social processes, and provide researchers with an opportunity to observe participants’ body language; they also allow researchers to observe social interaction.
  • Focus groups can be expensive and time-consuming, as are one-on-one interviews; there is also the possibility that a few participants will dominate the group and silence others in the group.
  • Other types of qualitative research include case studies, ethnography, and phenomenology.
  • Data saturation – the point in the qualitative research data collection process when no new information is being discovered
  • Focus groups- planned discussions designed to elicit group interaction and “obtain perceptions on a defined area of interest in a permissive, nonthreatening environment” (Krueger & Casey, 2000, p. 5)
  • Moderator- the researcher tasked with facilitating the conversation in the focus group

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Qualitative Research: Characteristics, Design, Methods & Examples

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On This Page:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

  • Gain deep contextual understandings of the subjective social reality of individuals
  • To answer questions about experience and meaning from the participant’s perspective
  • To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

Examples of qualitative research questions include: 

  • How does stress influence young adults’ behavior?
  • What factors influence students’ school attendance rates in developed countries?
  • How do adults interpret binge drinking in the UK?
  • What are the psychological impacts of cervical cancer screening in women?
  • How can mental health lessons be integrated into the school curriculum? 


Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it. 

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.


This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.


Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

 This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.
  • Define a Research Question : To start content analysis, a clear research question should be developed.
  • Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
  • Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
  • Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
  • Code the Data : Follow the coding rules to categorize data into themes.
  • Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.
  • Define the Research Question : Develop a relevant research question to frame the analysis.
  • Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
  • Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
  • Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.
  • Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
  • Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
  • Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
  • Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
  • Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
  • Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.
  • Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
  • Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
  • Indexing : Apply the framework systematically to the entire study data.
  • Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
  • Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 


Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).


Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 


Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 


The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

  • Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
  • Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
  • Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

Boeije, H. (2014). Analysis in qualitative research. Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology , 3 (2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Brooks, J., McCluskey, S., Turley, E., & King, N. (2014). The utility of template analysis in qualitative psychology research. Qualitative Research in Psychology , 12 (2), 202–222. https://doi.org/10.1080/14780887.2014.955224

Busetto, L., Wick, W., & Gumbinger, C. (2020). How to use and assess qualitative research methods. Neurological research and practice , 2 (1), 14-14. https://doi.org/10.1186/s42466-020-00059-z 

Carter, N., Bryant-Lukosius, D., DiCenso, A., Blythe, J., & Neville, A. J. (2014). The use of triangulation in qualitative research. Oncology nursing forum , 41 (5), 545–547. https://doi.org/10.1188/14.ONF.545-547

Critical Appraisal Skills Programme. (2018). CASP Checklist: 10 questions to help you make sense of a Qualitative research. https://casp-uk.net/images/checklist/documents/CASP-Qualitative-Studies-Checklist/CASP-Qualitative-Checklist-2018_fillable_form.pdf Accessed: March 15 2023

Clarke, V., & Braun, V. (2013). Successful qualitative research: A practical guide for beginners. Successful Qualitative Research , 1-400.

Denny, E., & Weckesser, A. (2022). How to do qualitative research?: Qualitative research methods. BJOG : an international journal of obstetrics and gynaecology , 129 (7), 1166-1167. https://doi.org/10.1111/1471-0528.17150 

Glaser, B. G., & Strauss, A. L. (2017). The discovery of grounded theory. The Discovery of Grounded Theory , 1–18. https://doi.org/10.4324/9780203793206-1

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. doi:10.1177/1525822X05279903

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Koch, T. (1994). Establishing rigour in qualitative research: The decision trail. Journal of Advanced Nursing, 19, 976–986. doi:10.1111/ j.1365-2648.1994.tb01177.x

Lincoln, Y., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

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Petty, N. J., Thomson, O. P., & Stew, G. (2012). Ready for a paradigm shift? part 2: Introducing qualitative research methodologies and methods. Manual Therapy , 17 (5), 378–384. https://doi.org/10.1016/j.math.2012.03.004

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

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This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

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D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

  • Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
  • Wiley Stats Ref for background information on statistics topics
  • Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.


  • D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
  • Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
  • Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

  • IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
  • OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
  • Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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  • Last Updated: Apr 25, 2024 11:09 AM
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The Four Types of Research Paradigms: A Comprehensive Guide

The Four Types of Research Paradigms: A Comprehensive Guide

  • 5-minute read
  • 22nd January 2023

In this guide, you’ll learn all about the four research paradigms and how to choose the right one for your research.

Introduction to Research Paradigms

A paradigm is a system of beliefs, ideas, values, or habits that form the basis for a way of thinking about the world. Therefore, a research paradigm is an approach, model, or framework from which to conduct research. The research paradigm helps you to form a research philosophy, which in turn informs your research methodology.

Your research methodology is essentially the “how” of your research – how you design your study to not only accomplish your research’s aims and objectives but also to ensure your results are reliable and valid. Choosing the correct research paradigm is crucial because it provides a logical structure for conducting your research and improves the quality of your work, assuming it’s followed correctly.

Three Pillars: Ontology, Epistemology, and Methodology

Before we jump into the four types of research paradigms, we need to consider the three pillars of a research paradigm.

Ontology addresses the question, “What is reality?” It’s the study of being. This pillar is about finding out what you seek to research. What do you aim to examine?

Epistemology is the study of knowledge. It asks, “How is knowledge gathered and from what sources?”

Methodology involves the system in which you choose to investigate, measure, and analyze your research’s aims and objectives. It answers the “how” questions.

Let’s now take a look at the different research paradigms.

1.   Positivist Research Paradigm

The positivist research paradigm assumes that there is one objective reality, and people can know this reality and accurately describe and explain it. Positivists rely on their observations through their senses to gain knowledge of their surroundings.

In this singular objective reality, researchers can compare their claims and ascertain the truth. This means researchers are limited to data collection and interpretations from an objective viewpoint. As a result, positivists usually use quantitative methodologies in their research (e.g., statistics, social surveys, and structured questionnaires).

This research paradigm is mostly used in natural sciences, physical sciences, or whenever large sample sizes are being used.

2.   Interpretivist Research Paradigm

Interpretivists believe that different people in society experience and understand reality in different ways – while there may be only “one” reality, everyone interprets it according to their own view. They also believe that all research is influenced and shaped by researchers’ worldviews and theories.

As a result, interpretivists use qualitative methods and techniques to conduct their research. This includes interviews, focus groups, observations of a phenomenon, or collecting documentation on a phenomenon (e.g., newspaper articles, reports, or information from websites).

3.   Critical Theory Research Paradigm

The critical theory paradigm asserts that social science can never be 100% objective or value-free. This paradigm is focused on enacting social change through scientific investigation. Critical theorists question knowledge and procedures and acknowledge how power is used (or abused) in the phenomena or systems they’re investigating.

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Researchers using this paradigm are more often than not aiming to create a more just, egalitarian society in which individual and collective freedoms are secure. Both quantitative and qualitative methods can be used with this paradigm.

4.   Constructivist Research Paradigm

Constructivism asserts that reality is a construct of our minds ; therefore, reality is subjective. Constructivists believe that all knowledge comes from our experiences and reflections on those experiences and oppose the idea that there is a single methodology to generate knowledge.

This paradigm is mostly associated with qualitative research approaches due to its focus on experiences and subjectivity. The researcher focuses on participants’ experiences as well as their own.

Choosing the Right Research Paradigm for Your Study

Once you have a comprehensive understanding of each paradigm, you’re faced with a big question: which paradigm should you choose? The answer to this will set the course of your research and determine its success, findings, and results.

To start, you need to identify your research problem, research objectives , and hypothesis . This will help you to establish what you want to accomplish or understand from your research and the path you need to take to achieve this.

You can begin this process by asking yourself some questions:

  • What is the nature of your research problem (i.e., quantitative or qualitative)?
  • How can you acquire the knowledge you need and communicate it to others? For example, is this knowledge already available in other forms (e.g., documents) and do you need to gain it by gathering or observing other people’s experiences or by experiencing it personally?
  • What is the nature of the reality that you want to study? Is it objective or subjective?

Depending on the problem and objective, other questions may arise during this process that lead you to a suitable paradigm. Ultimately, you must be able to state, explain, and justify the research paradigm you select for your research and be prepared to include this in your dissertation’s methodology and design section.

Using Two Paradigms

If the nature of your research problem and objectives involves both quantitative and qualitative aspects, then you might consider using two paradigms or a mixed methods approach . In this, one paradigm is used to frame the qualitative aspects of the study and another for the quantitative aspects. This is acceptable, although you will be tasked with explaining your rationale for using both of these paradigms in your research.

Choosing the right research paradigm for your research can seem like an insurmountable task. It requires you to:

●  Have a comprehensive understanding of the paradigms,

●  Identify your research problem, objectives, and hypothesis, and

●  Be able to state, explain, and justify the paradigm you select in your methodology and design section.

Although conducting your research and putting your dissertation together is no easy task, proofreading it can be! Our experts are here to make your writing shine. Your first 500 words are free !

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6 Qualitative data examples for thorough market researchers

Types of qualitative data in market research, 6 qualitative data examples, get nuanced insights from qualitative market research.

There are plenty of ways to gather consumer insights for fresh campaigns and better products, but qualitative research is up there with the best sources of insight.

This guide is packed with examples of how to turn qualitative data into actionable insights, to spark your creativity and sharpen your research strategy. You’ll see how qualitative data, especially through surveys, opens doors to deeper understanding by inviting consumers to share their experiences and thoughts freely, in their own words — and how qualitative data can transform your brand.

Before we dig into some examples of how qualitative data can empower your teams to make focused, confident and quick decisions on anything from product to marketing, let’s go back to basics. We can categorize qualitative data into roughly three categories: binary, nominal and ordinal data. Here’s how each of them is used in qualitative data analysis.

Binary data

Binary data represents a choice between two distinct options, like ‘yes’ or ‘no’. In market research, this type of qualitative data is useful for filtering responses or making clear distinctions in consumer preferences.

Binary data in qualitative research is great for straightforward insights, but has its limits. Here’s a quick guide on when to use it and when to opt for qualitative data that is more detailed:

Binary data is great for:

  • Quick Yes/No questions : like “Have you used our app? Yes or No.”
  • Initial screening : to quickly sort participants for further studies.
  • Clear-cut answers : absolute factors, such as ownership or usage.

Avoid binary data for:

  • Understanding motivations : it lacks the depth to explore why behind actions.
  • Measuring intensity : can’t show how much someone likes or uses something.
  • Detail needed for product development : misses the nuanced feedback necessary for innovations.

qualitative research 4 types

Nominal data

Nominal data categorizes responses without implying any order. For example, when survey respondents choose their favorite brand from a list, the data collected is nominal, offering insights into brand preferences among different demographics.

Some other examples of qualitative data that can be qualified as nominal are asking participants to name their primary information source about products in categories like social media, friends, or online reviews. Or in focus groups, discussing brand perceptions could classify brands into categories such as luxury, budget-friendly, or eco-conscious, based on participant descriptions.

Nominal data is great for:

  • Categorizing responses : such as types of consumer complaints (product quality, customer service, delivery issues).
  • Identifying preferences : like favorite product categories (beverages, electronics, apparel).
  • Segmentation : grouping participants based on attributes (first-time buyers, loyal customers).

Nominal data is not for:

  • Measuring quantities : it can’t quantify how much more one category is preferred over another.
  • Ordering or ranking responses : it doesn’t indicate which category is higher or lower in any hierarchy.
  • Detailed behavioral analysis : While it can group behaviors, it doesn’t delve into the frequency or intensity of those behaviors.

qualitative research 4 types

Ordinal data

Ordinal data introduces a sense of order, ranking preferences or satisfaction levels. In qualitative analysis, it’s particularly useful for understanding how consumers prioritize features or products, giving researchers a clearer picture of market trends.

Other examples of qualitative data analyses that use ordinal data, are for instance a study on consumer preferences for coffee flavors, participants might rank flavors in order of preference, providing insights into flavor trends. You can also get ordinal data from focus groups on things like customer satisfaction surveys or app usability, by asking users to rate their ease of use or happiness on an ordinal scale.

Ordinal data is great for:

  • Ranking preferences : asking participants to rank product features from most to least important.
  • Measuring satisfaction levels : using scales like “very satisfied,” “satisfied,” “neutral,” “dissatisfied,” “very dissatisfied.”
  • Assessing Agreement : with statements on a scale from “strongly agree” to “strongly disagree.”

Ordinal data is not for:

  • Quantifying differences : it doesn’t show how much more one rank is preferred over another, just the order.
  • Precise measurements : can’t specify the exact degree of satisfaction or agreement, only relative positions.

qualitative research 4 types

This mix of qualitative and quantitative data will give you a well-rounded view of participant attitudes and preferences.

The things you can do with qualitative data are endless. But this article shouldn’t turn into a work of literature, so we’ll highlight six ways to collect qualitative data and give you examples of how to use these qualitative research methods to get actionable results.

qualitative research 4 types

How to get qual insights with Attest

You can get to the heart of what your target customers think, with reliable qualitative insights from Attest Video Responses

1. Highlighting brand loyalty drivers with open-ended surveys and questionnaires

Open-ended surveys and questionnaires are great at finding out what makes customers choose and stick with a brand. Here’s why this qualitative data analysis tool is so good for gathering qualitative data on things like brand loyalty and customer experience:

Straight from the source

Open-ended survey responses show the actual thoughts and feelings of your target audience in their own words, while still giving you structure in your data analysis.

Understanding ‘why’

Numbers can show us how many customers are loyal; open-ended survey responses explain why they are. You can also easily add thematic analysis to the mix by counting certain keywords or phrases.

Guiding decisions

The insights from these surveys can help a brand decide where to focus its efforts, from making sure their marketing highlights what customers love most to improving parts of their product.

Surveys are one of the most versatile and efficient qualitative data collection methods out there. We want to bring the power of qualitative data analysis to every business and make it easy to gather qualitative data from the people who matter most to your brand. Check out our survey templates to hit the ground running. And you’re not limited to textual data as your only data source — we also enable you to gather video responses to get additional context from non verbal cues and more.

2. Trend identification with observation notes

Observation notes are a powerful qualitative data analysis tool for spotting trends as they naturally unfold in real-world settings. Here’s why they’re particularly valuable insights and effective for identifying new trends:

Real behavior

Observing people directly shows us how they actually interact with products or services, not just how they say they do. This can highlight emerging trends in consumer behavior or preferences before people can even put into words what they are doing and why.

Immediate insights

By watching how people engage with different products, we can quickly spot patterns or changes in behavior. This immediate feedback is invaluable for catching trends as they start.

Context matters

Observations give you context. You can see not just what people do, but where and how they do it. This context can be key to understanding why a trend is taking off.

Unprompted reactions

Since people don’t know they’re being observed for these purposes, their actions are genuine. This leads to authentic insights about what’s really catching on.

3. Understanding consumer sentiments through semi-structured interviews

Semi-structured interviews for qualitative data analysis are an effective method for data analysts to get a deep understanding of consumer sentiments. It provides a structured yet flexible approach to gather in-depth insights. Here’s why they’re particularly useful for this type of research question:

Personal connection

These interviews create a space for a real conversation, allowing consumers to share their feelings, experiences, and opinions about a brand or product in a more personal setting.


The format lets the interviewer explore interesting points that come up during the conversation, diving deeper into unexpected areas of discussion. This flexibility uncovers richer insights than strictly structured interviews.

Depth of understanding

By engaging in detailed discussions, brands can understand not just what consumers think but why they think that way and what stations their train of thought passes by.

Structure and surprise

Semi-structured interviews can be tailored to explore specific areas of interest while still allowing for new insights to emerge.

4. Using focus groups for informing market entry strategies

Using a focus group to inform market entry strategies provides a dynamic way to discover your potential customers’ needs, preferences, and perceptions before launching a product or entering a new market. Here’s how focus groups can be particularly effective for this kind of research goal:

Real conversations

Focus groups allow for real-time, interactive discussions, giving you a front-row seat to hear what your potential customers think and feel about your product or service idea.

Diverse Perspectives

By bringing together people from various backgrounds, a focus group can offer a wide range of views and insights, highlighting different consumer needs and contextual information that you might miss out on in a survey.

Spotting opportunities and challenges

The dynamic nature of focus groups can help uncover unique market opportunities or potential challenges that might not be evident through other research methods, like cultural nuances.

Testing ideas

A focus group is a great way to test and compare reactions to different market entry strategies, from pricing models to distribution channels, providing clear direction on what approach might work best.

5. Case studies to gain a nuanced understanding of consumers on a broad level

Case studies in qualitative research zoom in on specific stories from customers or groups using a product or service, great for gaining a nuanced understanding of consumers at a broad level. Here’s why case studies are a particularly effective qualitative data analysis tool for this type of research goal:

In-depth analysis

Case studies can provide a 360-degree look at the consumer experience, from initial awareness to post-purchase feelings.

This depth of insight reveals not just what consumers do, but why they do it, uncovering motivations, influences, and decision-making processes.

Longitudinal insight

Case studies can track changes in consumer behavior or satisfaction over time, offering a dynamic view of how perceptions evolve.

This longitudinal perspective is crucial for giving context to the lifecycle of consumer engagement with a brand.

Storytelling power

The narrative nature of case studies — when done right — makes them powerful tools for communicating complex consumer insights in an accessible and engaging way, which can be especially useful for internal strategy discussions or external marketing communications.

6. Driving product development with diary studies

Diary studies are a unique qualitative research method that involves participants recording their thoughts, experiences, or behaviors over a period of time, related to using a product or service. This qualitative data analysis method is especially valuable for driving product development for several reasons:

Real-time insights

Diary studies capture real-time user experiences and feedback as they interact with a product in their daily lives.

This ongoing documentation provides a raw, unfiltered view of how a product fits into the user’s routine, highlighting usability issues or unmet needs that might not be captured in a one-time survey or interview.

Realistic user journey mapping

By analyzing diary entries, you can map out the entire user journey, identifying critical touch points where users feel delighted, frustrated, or indifferent.

This then enables you to implement targeted improvements and innovations at the moments that matter most.

Identifying patterns

Over the course of a diary study, patterns in behavior, preferences, and challenges can emerge, which is great for thematic analysis.

It can guide product developers to prioritize features or fixes that will have the most significant impact on user satisfaction, which is especially great if they don’t know what areas to focus on first.

Qualitative research brings your consumers’ voices directly to your strategy table. The examples we’ve explored show how qualitative data analysis methods like surveys, interviews, and case studies illuminate the ‘why’ behind consumer choices, guiding more informed decisions. Using these insights means crafting products and messages that resonate deeply, ensuring your brand not only meets but exceeds consumer expectations.

qualitative research 4 types

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  • Open access
  • Published: 31 May 2024

A qualitative study on inner experience of self-management behavior among elderly patients with type 2 diabetes in rural areas

  • Zi-chen Zhang 1 ,
  • Qiu-hui Du 1 ,
  • Hong-hong Jia 1 ,
  • Yu-min Li 1 ,
  • Yu-qin Liu 1 &
  • Shao-bo Li 1  

BMC Public Health volume  24 , Article number:  1456 ( 2024 ) Cite this article

101 Accesses

Metrics details

As a chronic metabolic disease, diabetes poses a serious threat to human health and has become a major public health problem in China and worldwide. In 2020, 30% of Chinese people (aged ≥ 60 years) reported having diabetes mellitus. Moreover, individuals with diabetes living in rural areas face a significantly higher mortality risk compared to those in urban areas. In this study, we explored the inner experience of self-management behaviors in elderly patients with type 2 diabetes in rural areas to inform targeted interventions.

A phenomenological research design was used to explore the inner experience of self-management in rural elderly diabetes. Ten elderly diabetic patients were sampled from December 2022 to March 2023 in rural areas of Yangcheng County, Jincheng City, ShanXi Province, China. The seven-step Colaizzi phenomenological was used to analyze the interview data and generate themes.

Four themes emerged: “Insufficient self-management cognition”, “Negative self-management attitude”, “Slack self-management behavior”, and “No time for self-management”.

The level of self-management among elderly patients with type 2 diabetes in rural areas is low. Healthcare professionals should develop targeted interventions aimed at enhancing their cognitive levels, modifying their coping styles, and improving their self-management abilities to improve their quality of life.

Peer Review reports

Diabetes mellitus, as a chronic metabolic disease, not only seriously threatens the health of human beings but also imposes a heavy economic burden on individuals, families, and societies. It has become a significant public health issue for our country and the world as a whole. According to survey data from the International Diabetes Federation in 2021 [ 1 ], diabetes affects up to 537 million adults aged 20 ∼ 79 years globally. In China, 140 million people have diabetes, ranking first in the world.

With the aging of the population, elderly people account for an increasing proportion of diabetes prevalence in China [ 2 ]. According to the 7th National Population Census of the National Bureau of Statistics, in 2020, approximately 30% of the 260.4 million elderly population (aged 60 years and above) had diabetes, with more than 95% of cases being type 2 diabetes mellitus (T2DM) [ 3 ]. Diabetes has emerged as a significant health concern among the elderly population in China. A study indicated that rural elderly patients with T2DM had a higher risk of mortality compared to urban patients [ 4 ].

The World Health Organization (WHO) states that improving the self-management behavior of patients with diabetes is the most effective intervention to improve their health. Good self-management can delay disease progression, reduce complications, improve patients’ quality of life, and reduce disability and mortality rates [ 5 , 6 ]. Compared to urban areas, lower economic incomes and lower levels of healthcare in rural areas result in poorer patient self-management [ 7 ]. Studies have primarily focused on quantitative research, such as surveys of the current status of self-management, influencing factors, and interventions in rural elderly with T2DM. However, there is a lack of qualitative studies specifically targeting self-management behaviors in this population.

Phenomenological research tends to focus on understanding the essence and significance of particular phenomena as perceived by those who directly experience them. It aims to capture the inner experiences and perspectives of participants [ 8 ]. Therefore, we use phenomenological research methods to conduct semi-structured interviews with elderly T2DM patients in rural China. The aim is to delve deeply into the inner experiences of self-management behaviors, providing new insights into the self-management behaviors of elderly T2DM patients in rural areas, and facilitating the development of more effective interventions.

In this phenomenological qualitative research, interviews and observations were used to collect data for conventional content analysis through a qualitative descriptive method conducted by the first author. Colaizzi’s seven-step analysis was employed to explain and classify textual data, considering the individual cultural and contextual effects on phenomena [ 9 ].


We used purposive sampling to select elderly patients with T2DM in rural areas of Yangcheng County, Jincheng City, Shanxi Province, China. Recruitment was primarily conducted by a nursing master’s student (first author), with the assistance of village doctors. Inclusion criteria included meeting the diagnostic criteria for diabetes in the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes in the Elderly (2020 Edition) [ 3 ]; being aged 60 years or above; possessing good expression and communication skills; and having no cognitive impairment. Sampling followed the principle of maximum differentiation, taking into account equality and representativeness in terms of gender, age, disease duration, and family history. We discontinued when the analysis reached data saturation: the interviewers encountered repetitive messages, without any new themes emerging from the interviews. As a result, a total of 10 patients were finally enrolled in this study.

The interview subjects were coded as N1 to N10 sequentially. Further information gathered from patients interviewed included demographic background, age, gender, course of the disease, level of education, and family history of diabetes. Informed consent has been obtained from the participants, parents of children, and legally authorized representatives in this study.

Data collection

The data collection was carried out from December 2022 to March 2023. All patients were informed of the study’s aim and agreed to participate in the in-depth interviews. The interviews were conducted in the patients’ home, and each interview lasted from 20 to 40 min. The researcher, who observed and recorded the patients’ tone of voice, intonation, facial expressions, body movements, and other non-verbal behaviors during the interview, listened carefully and recorded the entire interview using a tape recorder. The researcher comprehensively adopted the interview techniques including questioning, listening, responding, pursuing, and repeating, without guiding or implying the patients’ answers. The researcher encouraged the patients to express their own true views on the issues raised, creating a relaxed and natural atmosphere for the interviews. When the interview data became saturated and no new themes emerged, the interview was closed.

The final interview outline was formed based on the literature, the purpose of the study, and the feedback from the interviewees following the pre-interviews. The details are shown in Table  1 .

Data analysis

Within 24 h after the end of the interview, two researchers completed the transcription and cross-checked each other to ensure the accuracy of the content. Finally, one of the researchers submitted the original transcript to the patient for verification. The seven-step Colaizzi phenomenology was used to analyze the interview data [ 9 ].

Ethical considerations

The study was approved by the Ethics Committee of the Daqing Campus of Harbin Medical University. Before each interview, the researcher explained the purpose and content of the study to the interviewees and guaranteed confidentiality. Subsequently, informed consent was obtained from the participants, parents of children, and legally authorized representatives in this study.

This study included ten participants: four were male, and six were female. All participants had type 2 diabetes. Their ages ranged 61 to 77 years, with an average age of 71.6 years. The duration of the disease varied between 3 and 42 years, with a mean duration of 16.2 years. Among the participants, five had diabetes-related complications: one had a diabetic foot, two had diabetes complications with cardiovascular symptoms, and three had diabetic neuropathy. Three participants were illiterate, two had primary school education, and five had junior high school education. All participants were married, with two having experienced the loss of a spouse. Four participants had a family history of diabetes, and only one identified as Christian. Additionally, all participants were farmers. Six of the participants had other comorbidities, as detailed in Table  2 .

Data analysis led to the development of four themes and ten subthemes. The themes were: “Insufficient self-management cognition”, “Negative self-management attitude”, “Slack self-management behavior”, and “No time for self-management” (Table  3 ).

Theme 1: Insufficient self-management cognition

This theme includes two subthemes: lack of knowledge and limited access to information.

Lack of knowledge

The participants in this study lacked comprehensive and accurate knowledge about diet, exercise, and blood glucose monitoring in diabetes self-management.

Most of the participants(8 out of 10) expressed concerns about their diet. However, their dietary control was too one-sided, and they still held misconceptions in daily life. They believed that “eating less and avoiding sweets” is the right way to control blood glucose.

N4: “Diet is not just about avoiding sweets; it also involves eating smaller, more frequent meals. ” N5: “Eat everything but not in excess. ” N6: “My diet is not just avoiding sweets, I’m also cautious about eating excessive noodles.” However, the nutritional balance of diet components, such as the intake of fats and proteins, was ignored. N10: “Nowadays, whether it’s rice or noodles, I limit myself to one serving and don’t pay much attention to other accompaniments such as meat or additional dishes.”

Most of the participants (8 out of 10) considered labor and walking as exercise, but they did not consider the intensity of the exercise or calorie consumption. N2: “I don’t exercise intentionally, I just work all the time, planting seeds, and then doing housework.” N10: “I run a hostel, and my daily routine involves cleaning both upstairs and downstairs, which I consider as my exercise.”

Although all participants reported owning a home glucose meter, they were passive in monitoring their blood glucose levels. They relied on their own feelings to decide whether to monitor their blood sugar. N4: “I have not measured it for a long time, sometimes when I feel my blood sugar rising, I quickly take a measurement.”

Limited access to information

The educational level of elderly participants with T2DM in rural areas was generally low. Furthermore, the process of aging and the subsequent decline in memory function renders them incapable of effectively receiving information on disease self-management. N2: “I don’t read it because I don’t know the words or understand it.” N6: “I watch programs about diabetes on TV, but I tend to forget about it.”

Two participants mentioned that they were unable to consult with healthcare professionals during their hospitalization due to the staff being too busy. N2: “Sometimes I want to ask the doctor, but they are busy and unavailable.” N7: “During my hospitalization, the doctors were consistently occupied, spending their days working on their computers, while the nurses only appeared briefly to administer injections and fluids before departing.

Theme 2: negative self-management attitudes

I don’t care about that.

Some participants (5 out of 10) had a “go-with-the-flow” attitude toward diabetes. N6: “Diabetes varies for each individual, just go with the flow.” Two participants expressed more concern about whether the disease impacted their previous lifestyles than the disease itself. N8: “I don’t truly care about the disease, as long as I can eat, work, and play mahjong every day.” Due to her prior good health, the participant did not consider diabetes (a chronic disease) a serious matter. N9: “I used to be in good health, but I didn’t expect to get it, and I didn’t really pay much attention to it because there weren’t any symptoms.”

Negative lifeist

To a certain extent, the mindset and fatalism of elderly participants with T2DM in rural areas influence the disease management of these individuals. N1: “If I can, I will live two more days. If not, so be it. Birth, old age, sickness, and death are natural phenomena and unavoidable.” N4 (Christian): “The blessings and misfortunes of man’s life and death are determined by God. Since we believe in the true God, we have sustenance in our hearts, and God blesses us.”

Theme 3: Slack self-management behavior

Poor adherence.

One participant mentioned that his diet was more tailored to his own preferences rather than strictly following the doctors’ recommendations. Two participants indicated that their previous lifestyle habits prevented them from controlling their diet. N2: “I have no way to control it, and I don’t have many taboos, I still eat the same porridge, cornmeal, sweet potatoes, and stuff as before.”

For monitoring blood glucose, the participant (N10) mentioned, “when I first came back from the hospital, I kept measuring my blood glucose. However, I found it troublesome, so I stopped measuring it frequently, and now I just measure it once in a while.” Due to the invasive nature of blood glucose monitoring, participants may not be able to adhere to it for an extended period. N3: “I don’t want to measure my blood sugar at all because it hurts my fingers.”

Some participants (5 out of 10) tended to reduce the dosage or stop taking their medication without doctor’s guidance because they experienced hypoglycemia after taking it. N7: “I take Metformin and insulin. However, if I take it on time, I tend to experience hypoglycemia, so I will take it every two days.” N8: “Only take metformin, but I still experience hypoglycemia occasionally, so I reduced the dosage by myself.”

The disease-related factors severely hindered participants’ self-management behaviors. Four participants attribute elevated blood glucose levels to age. N7: “As people get older, their physical functions decline, and their blood sugar levels cannot remain the same as when they were younger.” Relaxation in self-management is due to the prolonged course of the disease. N8: “As the disease progressed and I remained asymptomatic, I gradually became more daring and ate whatever I wanted.” Three participants even indicated that diabetes was a lifelong diaease that was difficult to cure, which diminished their self-confidence in self-management. N4: “Diabetes is an exhausting disease that cannot be completely eradicated, and it is both physical and mental painful.”

Based on their own past experiences, most of the participants (7 out of 10) subjectively believed that their disease was well-controlled or that they were better at self-management than their reference subjects through comparisons with the outside world. However, this belief was often misleading. N1: “As far as I can tell, everything seems fine. I didn’t have any of the diabetic complications they talk about on TV, so I believe I’m managing it quite well.” N2: “I’m in pretty good condition. Compared to some people in our village who are on insulin and don’t control their diet, I’m doing much better.”

Theme 4: no time for self-management

Family role factors.

The traditional Chinese family model determines that women take on more responsibilities and obligations in their daily lives. N2: “I’m the one in charge of the family, and I don’t have the time and energy to manage this disease.” The shift in family roles also limited the participant’s self-management of the disease. N5: “Since my son got married, I have been living in a large household, and my dietary habits have become more aligned with theirs. Additionally, with grandchildren around, it is difficult to find time for exercise when I have to take care of them every day.

Economic factors

Compared to urban areas, the rural economy is insufficient to support the requirements of self-management. N2: “There are certain dietary recommendations that we (farmers) cannot fulfill. The doctors often advise us to eat more vegetables, but some are too expensive for us to afford. This differs from urban patients who have pensions and manage the expense.” N10: “Our rural conditions are poor and unsophisticated in many respects, there are many things we are unable to accomplish.”


In addition to diabetes, six participants in this study had other diseases, such as high blood pressure or heart disease, among others. N3: “I had hip surgery, and then I couldn’t exercise because my knees were not comfortable either.”

Participants in this study had some control over their diabetes diet and exercise but did not meet the requirements for disease management. They prefer to manage their blood glucose solely through medication intake and show poor adherence to blood glucose monitoring. Elderly patients with T2DM in rural areas often lack knowledge of disease management and fail to recognize the importance of self-management. The reason may be that elderly patients with T2DM in rural areas have weak self-care awareness, limited understanding of diabetes knowledge, lack awareness of its serious harms, and lack confidence in disease treatment and control, resulting in a low level of self-management. This is consistent with the results of Dongjing et al’s research on the self-management level of elderly diabetic patients in rural areas [ 10 ].

In this study, patients acquired knowledge and information about diabetes through doctors’ health education, peer sharing, and online sources. Moreover, due to the low levels of education among the participants (with the highest level being junior high school, and some of them (5 out of 10) having only primary school education or even being illiterate), their ability to comprehend information is limited. The acquisition of correct and effective knowledge and information about diabetes is crucial for patient self-management [ 11 , 12 ]. Therefore, as healthcare professionals, we should develop personalized and targeted health education initiatives according to the actual situation of rural elderly patients with T2DM, combined with their weaknesses in self-management. The health education methods should be diversified, such as video presentations, interactive lectures, and social media campaigns, to ensure patients gain a clear understanding of diabetes and its management, thus facilitating the effective implementation of self-management. Additionally, patients mentioned that they forgot the content of health education due to age-related memory loss, which also suggests that, in addition to implementing various health education, the frequency of health education should be increased to reinforce the memory of knowledge for elderly patients.

The participants hold a negative attitude towards the disease and its self-management. The reasons for this include, firstly, that rural elderly patients with type 2 diabetes have not fully comprehend the severity of the disease, nor the significance and necessity of self-management; secondly, they have not entirely embraced the reality of their diagnosis; and thirdly, traditional stereotypical ideological concepts have influenced the patients, fostering a negative attitude towards the disease.

Different attitudes towards the disease affect the self-management of patients with diabetes significantly [ 13 ]. If a patient holds a positive attitude, they will initiate strategies to adjust themselves and seek changes to better manage the disease [ 14 ]. Conversely, a patient with a negative attitude will tend to be more inclined to avoid the problem, resulting in poorer self-management behaviors [ 15 , 16 ]. This suggests that healthcare professionals should offer more humanistic care during patients’ hospitalization. After discharge, healthcare professionals should follow-up by telephone, communicate with patients more frequently, pay attention to the mental health of patients, provide timely and effective psychological guidance, and shift patients’ negative attitude towards the disease, so that they can face it disease correctly.

In this study, patients showed poor compliance with diet, medication, and blood glucose monitoring. Given that diabetes is a chronic disease, patients tend to lose confidence in self-management over time. And, feeling good about their condition can also lead to a lack of motivation for self-management. Consequently, healthcare professionals should explore methods to enhance external motivation in order to increase patients’ behavioral initiatives. Peer education is a form of sharing ideas and exchanging knowledge among a specific group of people, using the influence of peers to achieve educational goals [ 17 ]. During the interviews, it was found that patients were more inclined to communicate with their peers and share their knowledge and experiences in disease self-management because they believed that their peers could better understand their feelings. Moreover, previous studies have shown that peer support education can effectively increase patient participation and enhance glycemic control [ 18 , 19 ].This indicates that primary care providers can form peer support groups where members share daily diet, exercise, and blood glucose monitoring results, among other aspects. Those with good glycemic control can share their successes and answer questions, creating a positive atmosphere for self-management.

This study found that family, economic, and multimorbidity status hinder disease management in rural elderly patients with T2DM. As a chronic disease, diabetes requires long-term treatment and management. Family support plays an important role in the self-management of diabetic patients in rural China [ 20 ]. Health education involving family members is more conducive to patients’ glycemic control and significantly promotes self-management behaviors [ 21 ]. We should establish a family-centered social support system, encourage family members to participate in the daily management of diabetic patients, and urge patients to improve their attention to the disease and self-management ability. In addition, six participants in this study had multiple comorbidities and a serious disease burden. This indicates that relevant departments should formulate corresponding healthcare policies for rural patients with multiple coexisting diseases, such as opening outpatient chronic disease subsidies and reimbursements, to reduce their economic burden and promote self-management.


There are also some limitations in this study. Firstly, all patients were recruited only from rural areas within the same province in China, which introduces a degree of bias due to regional selection and study conditions. Secondly, the results of this study don’t necessarily apply to all diabetic patients. Thirdly, due to the cultural differences between native Chinese and English, translating the interviews from Chinese to English was another limitation of this study.


In this study, we used phenomenological research methods to delve deeply into the inner experience of self-management behaviors among rural elderly patients with T2DM. Through thorough analysis and refinement of the data, four themes emerged: insufficient self-management cognition, negative self-management attitudes, slack self-management behaviors, and no time for self-management. Therefore, healthcare professionals should actively implement targeted and diverse interventions to enhance the self-management abilities of rural elderly T2DM patients, reduce the incidence of complications, and thus effectively improve their quality of life.

Data availability

The datasets generated and analyzed in this study are not publicly available due to the principle of confidentiality. They are available from the corresponding author upon reasonable request.

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We would like to thank the participants in the study, those rural elderly patients with type 2 diabetes, for sharing their inner experiences of self-management.

The study was financially supported by Humanities and Social Science Foundation of the Ministry of Education of China (22YJAZH035).

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Zichen Zhang and Qiuhui Du: Conceived the study, Analyzed the data, Collected the data, Authored or reviewed drafts of the paper, and approved the final draft. Honghong Jia: Conceived the study, Audited the initial analyses and interpretation, Authored or reviewed drafts of the paper, and approved the fnal draft.Yumin Li、Yuqin Liu and Shaobo Li: Collected the data, Authored or reviewed drafts of the paper, and approved the final draft.

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Zhang, Zc., Du, Qh., Jia, Hh. et al. A qualitative study on inner experience of self-management behavior among elderly patients with type 2 diabetes in rural areas. BMC Public Health 24 , 1456 (2024). https://doi.org/10.1186/s12889-024-18994-w

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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:


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.


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.


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.


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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  • Open access
  • Published: 03 June 2024

Offering extended use of the contraceptive implant via an implementation science framework: a qualitative study of clinicians’ perceived barriers and facilitators

  • Nicole Rigler 1 ,
  • Gennifer Kully 2 , 3 ,
  • Marisa C. Hildebrand 2 ,
  • Sarah Averbach 2 , 3 &
  • Sheila K. Mody 2  

BMC Health Services Research volume  24 , Article number:  697 ( 2024 ) Cite this article

Metrics details

The etonogestrel contraceptive implant is currently approved by the United States Food and Drug Administration (FDA) for the prevention of pregnancy up to 3 years. However, studies that suggest efficacy up to 5 years. There is little information on the prevalence of extended use and the factors that influence clinicians in offering extended use. We investigated clinician perspectives on the barriers and facilitators to offering extended use of the contraceptive implant.

Using the Consolidated Framework for Implementation Research (CFIR), we conducted semi-structured qualitative interviews. Participants were recruited from a nationwide survey study of reproductive health clinicians on their knowledge and perspective of extended use of the contraceptive implant. To optimize the diversity of perspectives, we purposefully sampled participants from this study. We used content analysis and consensual qualitative research methods to inform our coding and data analysis. Themes arose deductively and inductively.

We interviewed 20 clinicians including advance practice clinicians, family medicine physicians, obstetrician/gynecologist and complex family planning sub-specialists. Themes regarding barriers and facilitators to extended use of the contraceptive implant emerged. Barriers included the FDA approval for 3 years and clinician concern about liability in the context of off-label use of the contraceptive implant. Educational materials and a champion of extended use were facilitators.


There is opportunity to expand access to extended use of the contraceptive implant by developing educational materials for clinicians and patients, identifying a champion of extended use, and providing information on extended use prior to replacement appointments at 3 years.

Peer Review reports

The etonogestrel contraceptive implant is currently approved by the U.S. Food and Drug Administration (FDA) for 3 years of continuous use for the prevention of pregnancy [ 1 ]. However, there is evidence to support its use for up to 5 years while maintaining a low risk of pregnancy [ 2 , 3 , 4 ]. The off-label use of the contraceptive implant past its FDA-approved duration and up to 5 years is known as extended use. Importantly, the FDA supports off-label use of marketed drugs and medical devices so long as there is strong relevant published evidence [ 5 ]. Off-label use such as extended use of the contraceptive implant is common with many other reproductive devices and medications, including misoprostol for labor induction, the copper intrauterine device (IUD) for emergency contraception, and, prior to its recent FDA-approval for extended use, the 52 mg levonorgestrel (LNG) IUD for pregnancy prevention. The 52 mg LNG IUD was previously FDA-approved for 5 years, however strong published evidence demonstrated longer efficacy up to 8 years, leading clinicians to counsel on extended use and eventually contributing to updated federal guidelines [ 6 , 7 ].

Though there are clinicians who counsel patients on extended use of the contraceptive implant, many patients still undergo implant replacement after only 3 years of use [ 8 , 9 ]. Continuation rates of the contraceptive implant after 1 and 2 years of use is estimated to be at 81.7% and 68.7%, with the most common reason for early discontinuation prior to 3 years being changes to bleeding pattern [ 10 , 11 , 12 , 13 ]. Ali et al. report the most common reasons that patients decided to stop implant use in years 4 and 5: unspecified personal reasons, desired fertility, bleeding problems, and other medical reasons [ 4 ]. Additionally, a recent nationwide, web-based survey amongst a diverse group of reproductive health clinicians investigated the barriers and facilitators regarding extended use of the contraceptive implant up to 5 years [ 14 ]. The most common barriers found in the study were provider concerns about pregnancy risk and the current FDA approval for only 3 years of use. The key facilitators included strong published evidence supporting extended use and patient and clinician education on extended use. Other than these studies, the patient and clinician factors that facilitate and hinder widespread implementation of extended use of the contraceptive implant have not been explored.

Increasing implementation of extended use of the contraceptive implant across practice settings may decrease unnecessary procedures, devices, healthcare visits, and could improve access to, and satisfaction with, the contraceptive implant. Long-acting reversible contraceptive (LARC) methods such as the contraceptive implant and LNG IUD have significantly higher continuation and approval rates and are more efficacious at preventing pregnancy than non-LARC methods such as oral contraceptive pills and depot medroxyprogesterone acetate injection [ 12 , 15 , [ 16 ]. Given the continued high rates of unintended pregnancies in the United States and the consequential increase in healthcare costs and poor outcomes secondary to pregnancy complications, efficacious pregnancy prevention is an important public health objective and cost-saving measure [ 17 ].

Using a qualitative approach guided by an implementation science framework, the Consolidated Framework for Implementation Research (CFIR), [ 18 ] we sought to explore clinician perspectives on extended use of the contraceptive implant up to 5 years as well as the perceived barriers and facilitators for clinicians to offer extended use.

We conducted semi-structured interviews with 20 clinicians including obstetrics and gynecology generalists, family medicine physicians, complex family planning sub-specialists, and advanced practice clinicians. We recruited interview participants from a nationwide, web-based survey that assessed the prevalence of extended use of the contraceptive implant [ 17 ]. This study recruited respondents through email listservs for the Fellowship in Complex Family Planning, the Ryan Residency Training in Family Planning Program, women’s health nurse practitioners, and family medicine physicians, as well as private social media groups for obstetrician-gynecologists. The total reach of the survey was unknown, however, the study had a survey completion rate of 66.6% ( n  = 300/450). Of the 300 completed surveys, 290 respondents indicated their interest in being interviewed (96.7%).

Among the survey respondents, we invited 24 clinicians to participate in interviews, yielding an 83.3% response rate. We selectively recruited interview participants to enrich our sample, specifically focusing on clinician type, practice setting, and region of practice within the United States (U.S.). We also selected interview participants based on whether they always, sometimes, or never counsel on extended use to investigate a broad range of perspectives. For this study, offering extended use is defined as counseling on use past the current FDA-approved duration of 3 years and up to 5 years of use. Offering extended use can occur at any clinical encounter, including insertion appointments, replacement and removal appointments at or before 3 years, and general reproductive health appointments. Clinicians who always offer extended use were defined as those who counsel on extended use to patients who are considering or currently have the contraceptive implant. Clinicians who sometimes offer extended use were defined as those who counsel on extended use, but only to particular patients based on patient-specific factors such as body mass index or insurance coverage. Clinicians who never offer extended use were defined as those who never counsel on use of the contraceptive implant past 3 years of use.

The interview guide was created utilizing an implementation science framework that identifies factors for effectively enacting interventions [ 18 ]. The Consolidated Framework for Implementation Research (CFIR) is organized into 5 major domains: characteristics of the intervention, individual characteristics, inner setting, outer setting, and the process of implementation. The first domain, intervention characteristics, relates to the inherent qualities of the intervention, such as pharmacologic properties and side effects of the contraceptive implant when used up to 5 years. Individual characteristics relates to the roles and characteristics of individual patients and clinicians interacting with the intervention, such as educational background and type of insurance coverage. The inner setting domain assesses the internal setting in which an intervention will be implemented (i.e., clinic type, culture, and policies). The broader context in which an intervention will be implemented, including national policies and social norms is evaluated within the outer setting domain. Finally, the process of implementation domain explores the activities and strategies used to implement the intervention, such as educational materials or clinician and staff trainings on extended use.

We designed the interview guide around these specific domains with questions that aimed to identify targeted strategies to support successful implementation. The complete interview guide is in Appendix A . The interview guide was designed with input from clinicians who regularly prescribe contraception, including extended use of the contraceptive implant, as well as CFIR and implementation science experts. The Human Research Protection Program at our institution approved the study.

A single research team member conducted semi-structured interviews via secure video conference between July and August 2021. Interview participants provided informed consent. All participants were asked a full set of open-ended questions based on the interview guide, with focused follow-up questions to further investigate potential themes or to clarify points. All interviews were audio recorded, then transcribed. For data analysis, we used a content analysis approach to identify concepts and patterns within the dataset [ 19 ]. Themes arose deductively and inductively, with deductive themes identified from the CFIR domains and inductive themes arising from interview insights. Consensual qualitative research methods informed both our data analysis and coding process [ 20 ]. Three authors were involved in the thematic coding of the transcripts. Initially, 5 transcripts were independently coded then checked for inter-coder reliability. Any disagreements were discussed, and a consensus was achieved. The remaining transcripts were then coded by one of the three authors. Once all interviews were coded, major themes and representative quotes were identified. The research team utilized ATLAS.ti for analysis [ 21 ].

Between July and August 2021, we interviewed 20 clinicians from a variety of clinical settings, regions, and women’s health professions, achieving the intended diversity of perspectives (Table  1 ). Among participants, 7 (35.0%) always, 8 (40.0%) sometimes, 5 (25.0%) never offer extended use of the contraceptive implant (Table 2 ).

Characteristics of the intervention

We found that changes to bleeding pattern in or after the third year of use was a barrier to clinicians offering extended use of the contraceptive implant. The participants in this study noted that perceived increases in the irregularity or frequency of a patient’s bleeding makes extended use of the implant difficult for patients to accept. One clinician noticed that some patients correlate changes in their bleeding pattern with a perceived decrease in the efficacy of their implant:

"People who do start noticing changes in bleeding pattern […] [and] associating that with, ‘Oh, my implant is wearing out or becoming expired. I need to get this changed out."

-Complex Family Planning Specialist, Southwest, Academic Setting, sometimes offers extended use

The same clinician discussed that more research on bleeding patterns in the extended use period and potential treatments for implant-associated irregularities could be a facilitator of extended use:

"For bleeding, I think it would be awesome if there is a research study, looking at use of OCPs [oral contraceptive pills] to manage bleeding near the end of the use of an implant or near that three-year mark,, […] So that we could give people… Honestly, either a natural history or a, ‘Here’s how you can manage that if you do want to keep using your implant longer.’"

- Complex Family Planning Specialist, Southwest, Academic Setting, sometimes offers extended use

Information on the bleeding pattern in years 4 and 5 of use and how clinicians can address irregular bleeding during implant use may increase acceptability of extended use.

Individual characteristics

We found that insurance impacts whether a clinician offers extended use:

"I do sometimes have patients saying, ‘I might be changing jobs or I’m going to be turning 27 or whatever.’ And so insurance is a barrier and so they’re like, ‘I want the new one while I still have this insurance.’"

- Family Medicine Physician, Midwest, Community Setting, sometimes offers extended use

Many participants agreed with this concept and stated that acceptability of extended use depends on a patient’s perception of their future insurance status. Clinicians observed that if a patient believes they will have coverage for a replacement or removal in the future, they are more likely to pursue extended use of their implant. Conversely, one clinician discussed how lack of current insurance coverage could be a facilitator of extended use:

"So, I would generally offer extended use to people that didn’t have insurance and would have to self-pay. I would like go through the data with them so they wouldn’t have to pay like $1,000 to get a new implant because it could work another year, or people that were concerned about changing side effects at that time."

- Obstetrician-Gynecologist, Southwest, Academic Setting, sometimes offers extended use

Overall, clinicians perceived that patients’ concerns about current and future insurance coverage may affect acceptance of extended use.

Inner setting

This study found that having a champion of extended use at a clinician’s home or affiliate institution was a facilitator of extended use. Most clinicians in the study stated that it is or would be helpful to have someone who worked with them clinically that was knowledgeable on the data about extended use. When asked which factor would promote extended use of the implant the most, this clinician stated:

"…having a champion who is really ready to present the evidence, because the evidence can be there, but people don’t have time to read it. If it’s not brought to them, they’re not really going to know about it."

- Obstetrician-Gynecologist, West Coast, Community Setting, does not offer extended use

Potential champions identified were physicians, nurses, medical directors, or other clinicians in leadership positions, but participants generally believed that the position should be held by someone who is passionate about contraception, highly familiar with the specific setting, and knowledgeable about the clinical studies on extended use.

A barrier noted by a few participants was the effect of discordant counseling by different clinicians, sometimes within the same clinic, on acceptability of extended use:

"I mean, I guess like getting everyone on the same page, like in your practice can be a barrier. Especially in the practice I’ve been at, which like I said was in a state that was very litigious, so people weren’t always willing to like go outside guidelines that were… So getting your whole group on the same page so patients get like a more consistent message."

- Obstetrician-Gynecologist, Southwest, Academic Setting, sometimes offers extended use.

Participants discussed that it is important for clinician teams to relay a cohesive message to patients, especially in settings where patients may see multiple clinicians for their contraceptive care.

Outer setting

Lack of FDA approval for extended use was identified as barrier by many clinicians, and some clinicians counseled patients only on the FDA-approved duration of the contraceptive implant:

"So, generally in our practice we don’t really talk about extended use. We say this is FDA approved for three years."

- Advanced Practice Clinician, Southeast, Community Setting, sometimes offers extended use.

Even clinicians who do offer extended use of the implant noted that off-label use can be confusing to patients, making it difficult to counsel on extended use:

"So I have patients all the time, who’ll say, ‘Well, what do you mean I can keep X, Y or Z in for an extra year?’ And I’ll say, ‘We have big studies that tell us that this is an okay thing to do.’ But that just feels weird. People don’t necessarily understand the role of the FDA or sort of how it works. And so it’s something like extended use just might be a really such a foreign concept. Right? It’s so far outside. But I think that there are also, there are lay outlets that cover this stuff. So it’s not that it’s impossible to access. It’s just that the patient has to be interested just like the provider has to be interested."

- Complex Family Planning Specialist, East Coast, Academic Setting, sometimes offers extended use.

Clinicians also observed that certain clinics must follow official guidelines without the flexibility to offer extended use, regardless of a clinician’s perspective or willingness to counsel on extended use. Interestingly, patient confusion as well as mistrust of the healthcare system may impact patient acceptability of extended use in the context of a three-year FDA-approved duration:

"The other thing is the FDA approval because the box says three years, but then like I tell people, you can take it out in five years. And then they don’t believe… Like who is right. Is it my doctor who’s getting in front of me right or the box, right?"

- Family Medicine Physician, West Coast, Community Setting, always offers extended use.

This clinician noted that a disconnect between a clinician’s counseling and prescription information may lead patients to be confused about the recommendation for extended use.

Another barrier mentioned by a few participants was provider concern about liability in the event of an unintended pregnancy. Participants discussed fear of both legal and interpersonal repercussions of unintended pregnancy after counseling on off-label use of a contraceptive device:

"Even though there’s a slim chance that a patient would get pregnant on Nexplanon [the contraceptive implant], I feel like if we were to say, ‘Yeah, you can use it beyond the four years,’ and they come up and they get pregnant, they’re that 1% chance that gets pregnant, I feel like there could be a little bit of blame laid on us if we were to tell them that they’re able to it beyond the three years when the label doesn’t say that yet."

- Advanced Practice Clinician, Southeast, Private Practice, does not offer extended use.

Some participants felt that they would “have no ground to stand on” in the event of a lawsuit (OBGYN Physician, Midwest, Private Practice), making them concerned about the possibility of increased liability in counseling on off-label use without FDA approval.

Interestingly, multiple clinicians also discussed abortion restrictions in the United States as influencing patients in their decision to pursue extended use or not:

"In the past four years [2017–2021] have also had a lot of patients express concern about the administration. And so wanting to kind of be as current as they can be with their devices and so potentially exchanging them sooner than they need."

- Complex Family Planning Specialist, West Coast, Academic Setting, always offers extended use.

Clinicians observed that patients are noticing and reacting to abortion restrictions when making their contraceptive decisions, which may impact the widespread implementation of extended use.

Process of implementation

Many clinicians reported that a barrier to implementing extended use was patient preference for removal when they are already in clinic for a scheduled removal or replacement procedure, regardless of being counseled on extended use at that time:

“’Oh, I’m already here. I’m approved. Let’s just go ahead and get it done.’ So there’s probably not a whole lot you can do about that either, once they’re already in the clinic, and have their mind set on it.”

- Obstetrician-Gynecologist, Southeast, Academic Setting, does not offer extended use.

Many participants in this study noted that patients have made logistical arrangements prior to their appointments including paid time off, childcare, or prior authorization. It can be difficult for clinicians to offer extended use within this context, therefore counseling is better done prior to a patient coming in for a replacement appointment.

A perceived facilitator of extended use that was mentioned often was clear, concise clinician educational services or materials that illustrates existing data on efficacy and risks. Clinicians believed that this education could be in the form of continued medical education, targeted trainings, or written summaries of relevant studies, data, and recommendations. One consistency across interviews was that education on extended use must be integrated into regular practice and be easily understood by busy clinicians:

"I think that when we get a pamphlet or a brochure or a one page, something that just has everything condensed so it’s a really quick, oh, okay, this is something that we can be offering patients. And these are the reasons why it would be a benefit to them, and these are the patients that maybe would fall out of not offering this to. I think because of how busy we are, that’s the best way for us to make change."

- Advanced Practice Clinician, Southwest, Academic Setting, does not offer extended use.

Participants reported that these resources should be widely distributed beyond the complex family planning and obstetrician-gynecology community to increase accessibility to extended use.

Another potential facilitator identified was effective patient educational materials such as flyers that state the 5-year efficacy of the contraceptive implant, though producing these might require FDA approval. Participants in this study report that patients rely on clinicians to provide information on the efficacy and duration of their contraceptive implant. However, it is difficult for patients to accept extended use when there are inconsistencies across multiple sources of information:

"I mean, if online, there was information where it said you can keep it in for three to five years and they’re able to back that up. You know, people like to do their own research. I think that would be helpful, versus it says everywhere three, three, three, three, three, and then you’re the only person telling them something different, then it’s a little more tricky."

- Obstetrician-Gynecologist, West Coast, Community Setting, does not offer extended use.

Overall, participants in this study expressed that it would be helpful to have easily understood information for clinicians and patients that explained the evidence for extended use.

Our results demonstrate that there is an opportunity to increase widespread implementation of extended use through multiple interventions. Clinicians reported that patients prefer to have their implants replaced when they are already in clinic for the procedure. Therefore, intervening prior to replacement appointments at 3 years in the form of telemedicine visits or notifications from scheduling staff may make extended use of the contraceptive implant more acceptable to patients. Further, clinician and patient education on extended use that is easily understood and widely disseminated would likely increase use of the contraceptive implant up to 5 years.

The implementation of extended use of the contraceptive implant up to 5 years likely decreases healthcare costs secondary to fewer procedures and unintended pregnancies, and expands reproductive choices for patients seeking contraception. It has been found that clinicians who offer extended use state that most of their patients accept extended use when it is offered [ 14 ]. However, the reasons why a patient may or may not accept extended use are unclear, but may include changes in bleeding and concerns about use past the FDA-approved duration. Research on bleeding patterns in the extended use period may facilitate counseling and give patients a better expectation of possible changes they may see in years 4 and 5. Additionally, research on the patient perspective and acceptability of using the contraceptive implant past its FDA-approved timeframe is needed.

This study focused on clinicians and their perspectives on extended use. However, it is important to note that patients may be fully informed about extended use and choose to replace their implant at or before 3 years of duration. All discussions regarding contraception, including extended use of the implant, should always occur within a patient-centered and shared decision-making model. Widespread offering of extended use may allow for more patients to make fully informed decisions about the duration and use of their contraceptive devices, therefore expanding reproductive choice and agency in addition to potentially sparing patients from unnecessary procedures and extra healthcare costs.

Interestingly, although there are data to reflect high implant efficacy in years 4 and 5, [ 2 , 3 , 4 ] some participants in this study believe there is increased liability in counseling on off-label use without FDA approval. Importantly, off-label use is common among reproductive clinicians and is protected by the FDA if there is strong published evidence supporting off label use [ 5 ]. Additionally, the Society of Family Planning supports extended use of the contraceptive implant up to 5 years [ 22 ]. The FDA requires implant training for clinicians before they can insert or remove the implant. This training includes the FDA product labeling indicating the maximum duration of use for pregnancy prevention as three years [ 1 ]. It is possible that clinician training and product labels that advertise a 3-year duration dissuade clinicians from offering extended use of the contraceptive implant due to concerns about legal repercussions in the event of an unintended pregnancy with extended use. Therefore, organization- or systems-level guidelines, policy changes, and trainings in support of extended use may allow clinicians to feel comfortable offering off-label use of the implant. Additionally, FDA approval of the contraceptive implant to 5 years would likely greatly facilitate implementation of extended use.

Changing the FDA label to reflect extended use can be expensive, and contraceptive companies may not be incentivized to change the label. However, increasing the FDA approval of the contraceptive implant would allow for companies to have a longer-acting contraceptive device that is more directly comparable to other LARC devices such as the 52 mg LNG IUD that can be used for up to 8 years. If FDA approval for 5 years of use were to occur, it is not known if the barriers described in this study would continue to apply. However, it is likely that the facilitators of extended use from this study would support implementation of extended use irrespective of the federally approved duration.

One strength of the study is the national sample and the diversity of clinician types and settings. There is also representation of clinicians who consistently offer extended use and those who do not offer extended use. Another strength of this study is that it was designed utilizing a framework focusing on implementation, thus yielding results that can be used to create effective interventions.

Limitations of this study include the small sample size and selection bias from recruiting from a prior study that utilized listservs and social media. Additionally, we recruited from a population that was specifically interested in family planning and identified mostly as Caucasian and female. Because of this, our results may not be generalizable to the national population of clinicians who offer contraceptive implant services. However, our direct selection of participants who only sometimes or do not offer extended use allowed us to hear diverse perspectives regardless of prior knowledge or interest in extended use. Another limitation is that we did not ask advanced practice clinicians what their specific training was (i.e., nurse practitioner or physician’s assistant). As the training for advanced practice clinicians can vary greatly, our results may not be generalizable to all advanced practice clinicians.

In conclusion, this study describes the barriers and facilitators to widespread implementation of extended use of the contraceptive implant. These results offer new perspectives and potential strategies to increase widespread implementation of extended use of the contraceptive implant up to 5 years of use. Based on our findings, there is opportunity to expand access to extended use by developing educational materials for clinicians and patients, identifying a champion of extended use, and counseling on extended use prior to removal appointments at 3 years. Of note, these results should be viewed in the context of recent policy access issues regarding reproductive health and used to support patient-centered contraceptive choices, regardless of a patient’s decision to extend use of their contraceptive implant up to 5 years. It is important that clinicians and patients utilize shared decision making when discussing extended use of the contraceptive implant.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to being stored in a private, HIPAA-compliant database, but are available from the corresponding author on reasonable request.


Consolidated Framework for Implementation Research

Food and Drug Administration

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We thank the participants in this study.

This study was funded by Organon (Study #201908). The funder had no role in the study design, analysis, or interpretation of findings.

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SM is the principal investigator and lead data analysis, including qualitative coding, and dissemination of findings. She was also involved in study design and participant recruitment. NR was the primary interviewer and was involved in study design, recruitment, data management, data analysis, and dissemination of findings. GK and MH were involved with study design, recruitment, coordination of the study, IRB documentation, data analysis, and dissemination of findings. SA was involved with study design and dissemination of findings. All authors read and approved the final draft of the manuscript.

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S.M. is a consultant for Bayer and Merck. She has grant funding from Organon and receives authorship royalties from UpToDate. S.A. has served as a consultant for Bayer on immediate postpartum IUD use. The remaining authors report no conflict of interest.

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Rigler, N., Kully, G., Hildebrand, M.C. et al. Offering extended use of the contraceptive implant via an implementation science framework: a qualitative study of clinicians’ perceived barriers and facilitators. BMC Health Serv Res 24 , 697 (2024). https://doi.org/10.1186/s12913-024-10991-4

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The Retrospective Stressor Analysis (RSA): a novel qualitative tool for identifying causes of burnout and mitigation strategies during residency

  • Kristin L. Chrouser 1 ,
  • Laura Zebib 1 ,
  • Blake F. Webb 2 ,
  • Tandi Bagian 2 &
  • Timothy Arnold 3 , 4  

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Resident physicians are at an increased risk of burnout due to their high-pressure work environments and busy schedules which can lead to poor mental health outcomes and decreased performance quality. Given variability among training programs and institutions across the United States, stressors likely differ, and interventions must be tailored to the local context, but few tools exist to assist in this process.

A tool commonly used in adverse event analysis was adapted into a “retrospective stressor analysis” (RSA) for burnout prevention. The RSA was tested in a group of chief residents studying quality improvement and patient safety in veteran’s hospitals across the United States. The RSA prompted them to identify stressors experienced during their residencies across four domains (clinical practice, career development, personal life, and personal health), perceived causes of the stressors, and potential mitigation strategies.

Fifty-eight chief residents completed the RSA. Within the clinical domain, they describe the stress of striving for efficiency and clinical skills acquisition, all while struggling to provide quality care in high pressure environments. In the career domain, identifying mentors and opportunities for research engagement was stressful. Within their personal lives, a lack of time-constrained their ability to maintain hobbies, relationships, and attend meaningful social events while also reducing their engagement in healthy behaviors such as exercise, optimal nutrition, and attending medical appointments. Within each of these domains, they identified and described stress mitigation strategies at the individual, departmental, and national levels.

The RSA is a novel tool that can identify national trends in burnout drivers while simultaneously providing tailored prevention strategies for residents and their training sites.

Peer Review reports

Resident physicians are vital to the US healthcare system, but burnout rates among residents range from 17–94%, with variation by specialty and program [ 1 , 2 ]. Burnout is characterized by emotional exhaustion, depersonalization, and a reduced sense of personal accomplishment.[ 3 ] External factors like demanding work environments, high patient care standards, long hours, poor work-life balance, lack of mental health support, and mistreatment in the workplace, combined with internal factors such as perfectionist personality, neuroticism, and previous mental health diagnosis, heighten the risk of burnout [ 1 , 4 , 5 , 6 ].

Burnout can be detrimental to resident physicians’ personal and professional well-being, leading to decreased job satisfaction, increasing attrition, depression, substance use, and suicide [ 7 , 8 , 9 ]. Burnout can also have clinical implications, negatively affecting patient access and quality of care. Burned-out physicians are more likely to make medical errors, exhibit increased implicit and explicit biases, and become less productive [ 9 , 10 , 11 , 12 ]. Meta-analyses find that burnout among healthcare providers is associated with reduced patient satisfaction, quality indicators, and perceived patient safety [ 13 , 14 ]. Thus, patient safety, quality of care, and physician wellbeing are inextricably linked. Consequently, the Accreditation Council for Graduate Medical Education (ACGME) has enhanced requirements for residency program accreditation, emphasizing monitoring and maintaining well-being during residency training [ 15 ].

A recent review of interventions to reduce resident burnout notes the current literature is of marginal quality and results are inconclusive [ 16 ]. While self-care initiatives have been shown to alleviate burnout in some small samples, such interventions emphasize modification of internal factors. This shifts the responsibility onto residents and does not address the systemic and environmental factors that promote burnout. Studies suggest that interventions addressing external factors such as work-hour limitations, structured mentorship programs, and access to mental health programs are associated with decreased burnout among residents [ 16 , 17 , 18 , 19 ]. Bakker’s Job Demands-Job Resources Conceptual Model illustrates that burnout is a consequence of chronic work-related stress, when job demands exceed job resources and individuals can no longer cope [ 20 ]. Therefore, effective burnout prevention requires reduction of job demands and/or augmentation of job resources, and should address both internal and external risk factors unique to each specialty and residency program.

Given the ACGME’s interest in burnout prevention, many residency programs use standardized surveys to monitor resident burnout rates. Tailored information can be gleaned from measures such as the Mini ReZ, which assesses the impact of several common residency stressors (e.g., electronic health record, interruptions, sleep impairment) [ 21 , 22 ]. However, causes of burnout will likely change rapidly over time as institutions adopt new technologies, face novel challenges (like COVID-19), or respond to regulatory changes.[ 23 ] For example, the advent of the electronic health record (EHR) rapidly changed documentation processes and created new stressors that increased physician burnout [ 24 ]. In the context of a constantly changing environment, surveys that identify sources of burnout based on the existing literature may fail to promptly capture ever-shifting stressors. Surveys are confined to capturing trends in explicitly asked topics. This limits our ability to capture emerging or unknown stressors. Furthermore, there is a paucity of data on residents’ perceptions of the causes of burnout. Therefore, we need tools that can elucidate burnout drivers and potential prevention strategies in rapidly changing environments from the perspective of impacted individuals. This will aid resource allocation for high-yield improvements.

The adaptation of an adverse event analysis tool can leverage methods that are already familiar to residents in order to generate an understanding of burnout drivers and potential interventions. With growing evidence of the negative impact of burnout on providers, trainees, and patient care, institutions need innovative tools to monitor for new causes of burnout in changing environments. This will allow them to rapidly shift burnout prevention strategies when appropriate. This study aims to 1) characterize recent residency graduates’ perceptions of the drivers of burnout, 2) identify potential interventions for mitigating resident burnout, and 3) assess the utility of the RSA (retrospective stressor analysis) as a novel tool to identify a wider breadth of sources of resident burnout than found in the current literature as well as generate practical strategies to mitigate these causes.

Root-cause analysis (RCA) is a methodology to identify underlying causes of an adverse event and has been used in healthcare to characterize and help prevent future adverse outcomes [ 25 ]. Residents are typically familiar with RCA methodology, including the “five whys” and fishbone diagrams from their patient safety and quality improvement training. The final deliverable of an RCA is a list of “action items” to address or eliminate these underlying causes and prevent similar future problems. Similar to an RCA, this Retrospective Stressor Analysis (RSA) was designed to identify potential underlying causes of stressors and list possible corrective actions/prevention strategies (see Appendix A). The RSA has dual utility to 1) be used by institutions to explore resident perceptions of causes as a cohort and implement resident-derived interventions and 2) be used by residents as an opportunity for self-reflection on their own individual perceived burnout causes and identify actions they can personally take to mitigate burnout and improve resilience.

In March 2022, 87 VA Chief Residents in Quality Improvement and Patient Safety (CRQS) across 67 Veterans Affairs Healthcare Centers were given a homework assignment on Building Resilience/Preventing Burnout. Participants were instructed to recall stressors experienced during their residencies across four domains (clinical practice, career development, personal life, and personal health) that they felt increased their risk of burnout. Then they listed perceived causes of these stressors and potential prevention and mitigation strategies. After aggregating the deidentified data, we coded stressors and mitigation strategies and identified themes within each of the four domains (clinical practice, career development, personal life, and personal health). Evaluating each entry’s content and context, one author (KC) developed codes using thematic analysis. After compiling the initial codebook, a second coder (TA) coded 20% of the entries in each domain. Co-analysis agreement was > 80%, and disagreements were resolved by discussion. Given the large dataset, codes for stressors were then ranked by frequency within each domain and the top 15 illustrated as word clouds. Conceptual themes were identified within each domain.

Preventive strategies for each domain were compiled and categorized by intervention level (personal, departmental, national) and themes were identified. Participants received a deidentified compilation of prevention strategies as a resource to share with their medical education community. The University of Michigan Institutional Review Board reviewed this study and determined it to be exempt and waived ethical approval and consent to participate. The data are available from the corresponding author, Dr. Kristin Chrouser, upon request.

In 2022, 58 chief residents (67%) completed the RSA assignment. All responses were deidentified, so demographic information is not available. Participants identified 1020 stressors (306 clinical, 262 career, 247 personal life, and 205 personal health) and 569 mitigation strategies (165 clinical, 136 career, 133 personal life, and 135 personal health). Qualitative analysis of stressors and mitigation strategies revealed several themes within the four domains.

Themes from clinical practice domain stressors

Participants describe the stresses related to their clinical work (Fig.  1 ), such as high patient volume, patient acuity, challenging patient interactions, poor outcomes, and systems issues, including EHR frustrations, documentation hassles, administrative burden, and lack of backup. They highlighted challenges regarding the management of clinical work, such as striving for efficiency, admitting a lack of knowledge/experience, and asking for help. They were stressed by their adjustments to gaining seniority over the course of training related to role transition, acquiring leadership and teaching skills, and delegation challenges. They describe challenges related to their role as learners, such as time to study, gaining clinical knowledge, and learning procedures. They also describe their emotional experience/response to the stresses of their clinical role, including experiencing imposter syndrome, worry, the weight of responsibilities, emotion management, coping with mistakes, and facing inadequacies.

figure 1

Clinical Practice Domain: Stressors that increased burnout risk*

*Size of word correlates with frequency of theme

Themes from the career development domain stressors

Participants describe various aspects of career development they considered stressful—such as research, publishing, presentations, teaching, committees, and professional relationships (Fig.  2 ). Many find career planning and career choices difficult, including fellowship decisions. They also recognize challenges in finding and becoming good mentors. Balancing academic and personal priorities and time management were common struggles. They also describe the additional stress of learning to cope with bias, competitiveness, failure, burnout, and performance anxiety.

figure 2

Career Domain: Stressors that increased burnout risk*

Themes from the personal life domain stressors

Participants describe challenges in maintaining their personal lives as residents (Fig.  3 ). Lack of time is a common complaint, leading to difficulty separating work and home lives while describing concerns with a lack of opportunity to unwind from the stressors of residency. This includes inadequate time to invest in social life and maintain relationships with family and friends, eventually leading to erosion of social support. Social isolation was exacerbated by geographic separation from family support, moving to a new city, and COVID-19 restrictions.

figure 3

Personal Life Domain: Stressors that increased burnout risk*

Many felt that long work hours led to difficulty coping with home stressors such as caregiving for children, family, and pets. Schedule inflexibility during residency led to missing important social events and being unavailable to manage family emergencies. They also recognized the difficulty of home maintenance, finances, and chores due to a lack of time. Residents described their emotional experience/response to these stresses in their personal lives as leading to guilt and feeling overwhelmed.

Themes from the personal health domain stressors

Participants described various challenges in maintaining their personal health as residents (Fig.  4 ). They described that a lack of time led to an inability to maintain healthy habits such as exercise, nutritious meals, proper hydration, and adequate sleep. Accessing physical and mental healthcare for themselves was difficult due to their schedules and social pressure to prioritize work over healthcare needs. Similarly, due to the demands and expectations of residency, many found it difficult to take a day off when ill.

figure 4

Personal Health Domain: Stressors that increased burnout risk*

The struggle to cope and manage stress was a common complaint. Many participants noted this was exacerbated by the physical and mental stress of pregnancy and parenthood. They also described various emotional experiences related to their health: fear of COVID-19, feeling out of control, anxiety, and guilt for taking a sick day.

Mitigation strategies at the personal level

Table 1 outlines potential interventions to reduce burnout at various levels. For mitigation strategies on a personal level, many emphasized the importance of maintaining productivity through intentional organization within all domains. For example, within the clinical and career domains, their self-identified need for ongoing clinical learning could be achieved through setting clear goals and creating consistent study schedules, and within the personal domain, by scheduling designated time for relaxation, vacation, hobbies, and quality time with family. Interestingly, this also included creating time to prioritize one’s own health and attend medical appointments. Residents stressed the importance of determining clear personal goals, priorities, and setting expectations both at work and with family members. Many described the need to outsource home tasks, including house cleaning, grocery shopping, and childcare. Also, they described behaviors to automate healthy choices such as meal prepping, not purchasing unhealthy snacks, tracking water intake, and organizing resident meals with healthy options to maintain personal wellness.

Many residents commented on the importance of creating a team-like atmosphere in the work environment, including learning what tasks to delegate and consistently coordinating debrief sessions after adverse patient outcomes. They also discussed the importance of leveraging specific relationships, such as nursing staff, specialists, hospital resources, and asking for help from senior residents or faculty. Many advised the importance of adapting one’s mindset, such as adopting a reflective mindfulness practice, being vulnerable with peers and mentors, and reframing success and failure. A common theme was the importance of peer-to-peer relationships in discussing potential hurdles such as imposter syndrome and creating a culture where open discussion was encouraged.

Mitigation strategies at the departmental level

At the departmental level, participants emphasized the importance of mentorship and coaching. While some encouraged the importance of individually reaching out to potential mentors early and the utility of building peer relationships, others described the role institutions can play in creating mentorship programming. They desired mentors who would discuss imposter syndrome and failure and guide mentees through career and personal decisions. Many felt a need for significant shifts in culture to encourage open communication, sharing failures, and enhancing feedback mechanisms.

Some advised significant changes to patient care responsibilities, such as reduced work hours, capping the number of patients, and reduced note writing. Others described a need for greater standardization of clinical expectations such as templates for best practices, patient handoffs, consults, checklists, and “guides” for workflows in different clinic settings. Many described the need for augmenting the curriculum to include robust mechanisms for research support and increased training during orientation on communication skills, efficiency in the workplace, teaching, navigating difficult cases, managing imposter syndrome, and coping with failure.

Mitigation strategies at the national level

The most cited mitigation strategy across all domains was reducing resident duty hours. While many emphasized the role of institutions in complying with duty hour restrictions, further adjustments to duty hours require top-down implementation by the ACGME. Residents stated that there was a need for greater flexibility to utilize wellness days or sick leave. Given the stressors associated with family planning, many felt the ACGME and/or institutions should provide information and resources for cryopreservation, parental leave, and lactation. Lastly, increasing resident income was suggested as an effective strategy to alleviate resident budgetary stressors, accurately reflect work hours, and compensate some for the added stress of challenging work schedules such as jeopardy call schedules and night-float.

Burnout’s impact on physician well-being and quality of care is well established, and current rates are concerning [ 1 , 5 ]. Therefore, we need tools for ongoing assessment of the underlying causes of resident burnout and identification of potential interventions within local work environments. In this study, we describe the successful use of a novel tool, the retrospective stressor analysis (RSA), informed by the familiar RCA process, to characterize residents’ perceptions of burnout causes and potential mitigation strategies. To our knowledge, the field currently lacks alternatives to survey-based tools that will identify new sources of burnout and provide individuals and institutions with intervention strategies.

Participants in our study highlight how lack of time impacts their well-being in all four domains. This is not surprising as previous studies have shown a significant difference in the burnout rates of residents based on adherence to work-hour restrictions [ 26 ]. Similar to our findings, Mian et al., identified several common stressors among trainees that lead to burnout, such as overwork/sleep deprivation, emotional drain of caring for sick patients, lack of time for personal life outside training, and residency coinciding with major life events such as parenthood [ 27 ]. Related, Linzer et al. found burnout correlated with work-related conditions such as value alignment, teamwork, work control, and time pressures [ 22 ]. The RSA identified similar themes among participants. In addition, the RSA also provided greater context across the four domains to elucidate previously unacknowledged sources of stress, such as career decision-making, acquisition of leadership roles, and coping with adverse patient outcomes. To our knowledge, these have not been previously identified as potential sources of burnout within the resident population. Moreover, participants provided highly specific stressors, such as “variability in clinical preferences among attendings”, and potential mitigation strategies that program directors might find useful when restructuring expectations or generating standardized workflows.

While the RSA may be a novel resource for understanding burnout, there were some challenges with using it in practice. Despite clear instructions to list multiple causes, some participants did not identify more than one potential cause of their stressors, even though this is a common step used in root cause analysis. Failure to identify a variety of causes can reduce the diversity of proposed interventions. Despite this potential limitation, our cohort of residents still generated a large range of interventions for burnout prevention and mitigation. However, if RSA is used in smaller resident samples in the future without encouraging participants to provide a range of causes, this might generate a reduced range of interventions, thus limiting impact. The “personal life” domain was listed prior to “personal health,” so often participants included many factors in the personal life domain that would have been more appropriate under personal health, which made analysis of frequency by domain more challenging.

Moreover, proposed preventive strategies overwhelmingly focused on personal actions, although some participants suggested departmental/institutional/national policy reforms. The Institute for Healthcare Improvement’s Action Hierarchy Tool is used to assist RCA teams in identifying interventions with the strongest effect for sustained and successful system improvement [ 28 ]. Stronger actions are those that do not rely on human memory, such as architectural changes, forced functioning, removing unnecessary steps, and tangible involvement of leadership; while weaker actions, such as trainings and new procedures, rely on humans to remember to perform an action. Many of the recommended interventions identified using the RSA would be classified as weaker actions, as they rely on residents to remember and make time to perform specific tasks such as exercise, mindfulness, studying, and delegating in the clinical space. Future iterations of the tool should encourage the development of stronger intervention actions.

Although these challenges with RSA had minimal effect on data and analysis, our group refined the RSA tool for future data collection. Refinements included adjusting domain order to reduce categorization errors, adding reminders of QI tools useful in collecting a broader range of potential causes (5 whys, fishbone diagram), and providing examples of systemic preventive strategies (e.g., programs, policies). The revised RSA is available in Appendix B. Although our sample size was more than adequate to reach saturation for a qualitative study, the RSA’s usability, generalizability, and utility of our findings may vary among residents. The Chief Residents in Quality Improvement and Patient Safety were already familiar with RCA tools and methods, but this might not be the case for all residents, and future iterations may be informed by piloting the tool in varied resident populations. Participants also provided this data via a homework assignment, and even though they were assured their responses were confidential, fear of being identified by course directors may have influenced their responses. Demographics of participants were not collected, precluding any analysis based on specialty or gender. This is a limitation as burnout causes and mitigation recommendations might differ based on demographic categories.

The RSA provided findings consistent with factors known to contribute to burnout in the literature while generating a broader range of stressors than previously reported. RSA utilization can allow residency programs to identify emerging burnout drivers as medicine changes rapidly and provides a wealth of intervention ideas appropriate to the local context. Engaging residents in developing implementation strategies can serve the dual purpose of reinforcing skills applicable to adverse event analysis techniques and helping prevent resident burnout. Qualitative data assessment from the RSA could also be used by national associations to identify novel stressors and then generate new quantitative survey questions more appropriate for measurement within a larger population.

We adapted a familiar patient safety tool, root cause analysis (RCA), to create the retrospective stressor analysis (RSA) for burnout prevention. This novel tool allowed recent residency graduates to identify stressors they believe increased their risk of burnout and generate practical preventive strategies at personal, institutional, and national levels. Common themes highlighted the difficulty of inflexible schedules and lack of time invested in protective factors such as social support, mentorship, and healthy habits. The RSA is a novel tool that can identify national trends in the drivers of burnout while providing tailored prevention strategies for individuals, training sites, and the ACGME to consider for future implementation.


The opinions expressed in this presentation are the authors’ own and do not necessarily reflect the view of the Department of Veterans Affairs or the United States government.

Availability of data and materials

The data are available from the corresponding author, Dr. Kristin Chrouser, upon request.


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KC, TB, and TA conceptualized, developed, and designed the research study and tools. KC, TA, LZ, and BW analyzed and organized data. KC and LZ wrote the first draft. All authors edited manuscript drafts, read, and approved the final manuscript.

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Chrouser, K.L., Zebib, L., Webb, B.F. et al. The Retrospective Stressor Analysis (RSA): a novel qualitative tool for identifying causes of burnout and mitigation strategies during residency. BMC Med Educ 24 , 591 (2024). https://doi.org/10.1186/s12909-024-05571-3

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Women’s emotional roller coasters during pregnancy as a consequence of infertility: a qualitative phenomenological study

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Women experiencing infertility tend to experience distinct emotions during pregnancy following infertility. Individuals in this population often struggle with psychological and social challenges during pregnancy. This qualitative phenomenological study determines how infertility experiences affect mothers’ mental well-being during pregnancy as well as their experiences and emotions after becoming pregnant. A face-to-face, in-depth interview with 15 women with infertility was conducted between November 2022 and May 2023. The study adhered to ethical guidelines, with informed written consent obtained prior to interviews and voice recordings. The COREQ checklist is applied to follow the EQUATOR guidelines for reporting research and the data were coded using MAXQDA.20 software. A thematic analysis revealed four main themes and 15 codes. Main themes included “Over Emotional Burden,” “Overprotection,” “Overthinking,” and “Social Activity Changes.” Pregnancy after infertility is a unique and emotionally charged experience for women, encompassing a spectrum of feelings that can be difficult to express. The importance of caring for these women and receiving support from their partners, families, and healthcare providers should not be overstated. Healthcare providers should be aware of these emotional challenges so they can provide better support and counselling to improve women’s overall pregnancy experience. It is believed that empathetic communication and tailored support can significantly improve the psychological well-being of this population. Psychologists, psychiatrists, and midwives should also be more attentive to mothers’ emotional challenges and integrate comprehensive emotional support and provide coping mechanisms in perinatal care programs.

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Infertility has been a prevalent health problem in recent generations around the world (Fainberg & Kashanian, 2019 ). Couples with infertility generally struggle with socioeconomic, psychological, physical, and cultural consequences of this condition (Kianfar et al., 2023 ). Based on statistical analysis, 10–15% of the world’s population experience infertility (Bakhtiyar et al., 2019 ). This condition is considered a severe disability and ranks fifth in severity. Considering the causes and duration of infertility, it may significantly affect a couple’s psychological well-being (Borumandnia et al., 2022 ). According to the literature, nearly half of women seeking infertility treatments struggle with disorders aligned with posttraumatic stress disorder. Researchers have also noted that infertility concerns impair coping mechanisms in individuals which result in considerable stress and anxiety among infertile couples (Renzi et al., 2023b ). Nowadays, infertility procedure is often suggested as a double-edged sword that can cause considerable emotional distress, social stigma, and financial concerns in couples (Taebi et al., 2021 ). According to the literature, infertility treatment processes mainly affect women more than men, and infertile women are considered deficient women in some cultures and societies (Olma & Bir, 2018 ; Yao et al., 2018 ). According to a literature, the better quality of life of couples is strongly associated with more success rates in infertility treatments. It also appears that decreased psychological symptoms as well as enhanced emotion regulation abilities may contribute to improved success rates in treatment centers (Renzi et al., 2023a ). Pregnancy after infertility can be a unique and emotionally inspiring experience for infertile couples. However, even after achieving a successful pregnancy, women who have experienced infertility may struggle with psychological and social challenges during pregnancy and after childbirth (Crespo & Bestard, 2016 ). Studies have shown that women who get pregnant after infertility experience some levels of stress and anxiety regarding their fetal and maternal outcomes. Their pregnancy may also result in confusing feelings between excitement and grief (Maehara et al., 2022 ). In the first trimester, the infertile woman often struggles with fears of pregnancy loss, possible fetal abnormalities, and adapting to significant physical changes (Dornelles et al., 2016 ). During the third trimester, anxiety and stress can be caused by the fear of pregnancy complications and preterm labor. Furthermore, infertile women may face challenges in selecting a safe delivery method (Huang et al., 2019 ). According to the literature, there is considerable research on mental health before and after pregnancy in infertile couples (McMahon et al., 2011 ).

In this qualitative study, we aim to investigate how infertility experiences affect mothers’ mental well-being during pregnancy as well as their perspectives and feelings after becoming pregnant.

Study design

This research is a qualitative study with a Heideggerian, hermeneutic phenomenological approach. This study was framed by Heideggerian phenomenology, and its Heideggerian influence extends throughout the entire study. In this approach, personal experience and the interpretation of events is emphasized as significant components of understanding human cognition and behavior (Heidegger, 1996 ). As a method of studying human experience, hermeneutic phenomenology has the unique potential of being able to examine complex phenomena in depth. Methodologies like this are well suited for representing the depth and complexity of individual experiences, providing detailed context to understand the complex interaction of factors influencing individuals’ life experiences (Plager, 1994 ). All in all, this study uses hermeneutic phenomenology because of its suitability to explore human experiences of complex phenomena. This method involves analyzing and reflecting on participants’ life experiences in order to gain a deeper understanding of their subjective perceptions, interpretations, and understandings (Annells, 1996 ).

In this study, the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist is applied to follow the EQUATOR guidelines for reporting research. This is a 32-item checklist for reporting interviews and focus groups systematically. The checklist provides researchers with a framework for reporting key study design, data collection, and analysis aspects, fostering clarity and reproducibility. The criteria also cover various aspects of qualitative research, including data collection methods, participant recruitment, and analysis processes. Furthermore, following established reporting guidelines facilitates peer review by providing standardized criteria for assessing the validity and quality of qualitative research. In reporting qualitative research, the COREQ checklist emphasizes ethical considerations, such as ensuring participant privacy and providing a clear ethical rationale. Ultimately, adopting EQUATOR guidelines further improves qualitative research reporting standards, as well as improving qualitative research practices more broadly (Hope & King, 2022 ).

Participant selection

This study included participants who got pregnant after being diagnosed with infertility and agreed to participate in interviews. Researchers recruited participants from pregnant women who had experienced infertility and attended the Sakarya Training and Research Hospital’s perinatal clinic between November 2022 and May 2023. The interviewer, who was a registered midwife, obtained patient information from the hospital’s electronic medical records system. In order to recruit participants, A phone call was initially made to inform and gain permission from the participants, and then appointments were scheduled at convenient times and dates using social media and messenger applications such as WhatsApp and Telegram. Interviews took place in a quiet consulting room at the hospital, allowing open and honest communication. Interviews were conducted without the presence of other participants or researchers in order to preserve privacy and confidentiality. In this way, participants were able to share their experiences without concern for their privacy. Participants under 18 years of age and participants who withdraw from the study during the interview are excluded from the study. The registered midwife contacted 20 mothers initially and invited them to participate in the study. One mother declined participation due to her husband’s objection. Another mother cited privacy concerns as the reason for her non-participation. A third mother commented that her pregnancy period was challenging and she wished to forget it. The fourth mother moved to a different hospital and the final mother relocated to a different city in the second trimester. In this study, a comprehensive amount of data was collected and data saturation was deemed sufficient by the authors. A total of 15 mothers participated in the study. The interviews began with open-ended questions such as “How would you describe yourself before and after pregnancy?”, “How has the diagnosis of infertility affected your perspective on pregnancy?”, “How do you evaluate your relationship with your partner after becoming pregnant?”.

Pregnant women who experienced infertility and attended Sakarya Training and Research Hospital’s perinatal clinic between November 2022 and May 2023 were participants in the study. The interviews were conducted without the presence of any other participants or researchers.

Data collection

An in-depth interview was conducted with participants using a sociodemographic form, semi-structured interview form, and a TSCO audio recording device (Model no. TR906, Made in China). A written consent was obtained from participants before the interview and recording. The demographic characteristics of the participants were collected from the patients face-to-face in a quiet consulting room at the hospital. The interview took place during a period when patients were visiting the hospital for routine pregnancy check-ups. We decided on this for patient convenience and time efficiency. Audio recording permission was also obtained before the interviews began from the participants. In order to eliminate hesitation about the study and to encourage the interviewees to answer the questions honestly, the researcher explained to each interviewee that they would be assigned a number based on their order in the interview. After the interview, notes were taken while listening to the recordings. After obtaining written consent, completing sociodemographic forms, and scheduling face-to-face sessions, approximately 25–30-minute interviews were conducted. In qualitative research, the sample size is determined by the concept of data saturation. Data saturation occurs when no new information or themes emerge from the data, indicating that the sample size is adequate (Francis et al., 2010 ). According to our study, themes and insights recurred as the interviews progressed, indicating that the desired themes were thoroughly explored. Data saturation was considered sufficient in the study as all desired themes were adequately explored, and a comprehensive amount of data was collected.

Data analysis

In the study, two researchers contributed as data coders. The data was coded through thematic analysis to form related themes based on the document’s content. In qualitative data, themes can be identified, analyzed, and interpreted through thematic analysis. It emphasizes the active role of the researcher in the process of coding and theme development. Researchers organize and report their analytical observations using themes and psychiatrists are using these versions due to their greater flexibility (Clarke & Braun, 2017 ). Ultimately, 15 codes were determined along with four themes. The corresponding author (Ph.D. candidate) and second researcher (Ph.D.), who were experienced in analyzing qualitative data, classified and identified codes and themes through the study content. The first three authors of this study were female, and the last author was male. All of the authors have considerable experience in infertility and pregnancy and a keen interest in filling a gap in infertile women’s mental health during pregnancy. In the study, the themes were derived from the data rather than being predetermined, and through researchers’ analysis of participants’ responses and experiences, themes emerged initially. A MAXQDA Analytics Pro 2020 program was used to manage the data in this study.

Rigor and trustworthiness

The data’s trustworthiness was assessed based on the suggestions of Guba and Lincoln (Cypress, 2017 ; Guba & Lincoln, 1981 ). A trustworthiness concept can be categorized into credibility and dependability. The concept of credibility corresponds roughly to the concept of internal validity as defined by positive ideologues, while that of dependability corresponds to the concept of reliability. In terms of reliability, transferability, which is external validity, and confirmability, which relies mostly on presentation (Gunawan, 2015 ). The phenomenological approach was used in our study as a qualitative research design to verify the study’s credibility (Rodriguez & Smith, 2018 ). The last researcher, who was an obstetrics and gynecologist and specialized in infertility, reviewed the themes and verified the process’ accuracy. The concept of transferability is traditionally associated with the application or generalization of findings to a wide range of situations. However, qualitative research emphasizes the richness and depth of insights rather than strictly transferring them into other contexts. By examining individuals’ experiences within a specific context, qualitative research illuminates the intricacies and mechanisms underlying a phenomenon (Leung, 2015 ). Through member checking we allow participants to review and verify the findings as part of the research process.

Furthermore, we ensured that the findings were transferable and considered different viewpoints by incorporating multiple perspectives. In order to enhance the dependability of our data and ensure that our findings are accurate and reliable, we use an audit trail. In the audit trail approach, we tried to keep a detailed record of the research process, from decisions to data collection and analysis. A skilled researcher enhances the dependability of data through qualitative research principles, so we provided education courses with certificates for authors with little or no experience in qualitative studies. In order to enhance data confirmability, peer review strategies were used to ensure that the findings accurately reflect the participants’ perspectives and experiences, as well as the researcher’s interpretation, and enabled us to examine the data, analysis, and interpretation critically, ensuring the findings were trustworthy.

Ethical consideration

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Sakarya University on 05.10.2022 by the number E-71522473-050.01.04-171377-258. Informed written consent was obtained from participants before the interview and recording the voice, ensuring that they understood the study’s purpose, procedures, risks, benefits, and confidentiality. All participants were informed that they had the right to withdraw from the research at any time without consequence.

The demographic analysis revealed that the participants were between the ages of 22 and 38, with a mean age of 30.33 ± 4.19. There were 15 women; three graduated from universities, five from high schools, three from secondary schools, and four from primary schools. Eleven women were housewives, three were civil servants, and the remaining one was a family physician. The median duration of infertility was 3.73 years, ranging from 1 to 10 years. All participants except four mentioned female factors as a cause of infertility. One participant had both male and female factors. Two participants mentioned a malefactor, and another mentioned unexplained reasons. Regarding infertility treatment, seven participants got pregnant through IUI, and eight got pregnant through IVF. Some demographic characteristics of the participants are shown in Table  1 .

Mothers’ perspectives on emotional challenges during pregnancy after infertility revealed four themes and 15 codes: (1) Over Emotional Burden, (2) Overprotection, (3) Overthinking, (4) Social Activity Changes. Figure  1 shows the coding tree.

figure 1

Coding tree of the mothers’ psychological experiences in pregnancy after infertility

Theme 1: Over Emotional Burden

An infertility diagnosis can continue to impact the emotional well-being of the couple after pregnancy, making the perinatal period even more challenging. Pregnancy after experiencing infertility can be a journey filled with a complex combination of emotions, almost like a roller coaster ride of emotions. In the present study, pregnant women with a history of infertility reported intense emotional burdens, including Emotional Roller Coaster, Fear, Anxiety, Hopelessness, and Regret.

Emotional Roller Coaster

The roller coaster represents mothers’ unpredictable emotional ups and downs. Consequently, they feel overwhelmed and incapable of coping with their emotions. The emotional roller coaster women experience post-infertility is complex. An initial feeling of happiness and excitement can quickly turn into anxiety and uncertainty after a successful pregnancy.

“I am moving through some ups and downs emotionally. Actually, I express my emotional state to my husband a little bit, but he manages me well, thanks to him. Sometimes, I cry for no reason; sometimes, I get angry. Things happen momentarily.” (P1, female factor, 1-year infertility)
“Pregnancy was both a dream and a nightmare for me.” (P4, female factor, 4-years infertility)

Fear / Anxiety

It is common and understandable for women to feel fear and anxiety during pregnancy, especially following an infertility diagnosis. There may be a fear of miscarriage, preterm birth, or other complications that women experience during pregnancy, intensified by the memories of previous struggles. The women who participated in the current study stated that they often experience disappointment and uncertainty during pregnancy.

“In the early stages of my pregnancy, I was filled with fears and worries. I was afraid of miscarriage first, then … ” (P13, female factor, 8-years infertility)
“During the early stages of my pregnancy, I was very anxious. It was a fear in my mind to have a fake pregnancy.” (P3, female factor, 1-year infertility)


Hopelessness after long infertility treatment is a complex and reasonable emotional response in infertile couples. Many couples struggle with infertility for months or even years with repeated medical interventions and emotional challenges.

“I always thought pregnancy was impossible and unachievable for a long time.” (P13, female factor, 8-years infertility)
“There were many difficult experiences for me during infertility treatments, especially waiting in infertility clinics, which were emotionally draining for me. There were pregnant patients waiting in the reception hall. When I saw them, I thought it would never happen to me to be a mother.” (P9, female factor, 6-years infertility)

The experience of pregnancy may cause regret after years of infertility struggle. There are many factors that can contribute to regret, and it is a deeply personal emotion. It may result from a sense of lost control over one’s reproductive journey or from the impact it has had on one’s family and relationships.

“The first few months, I was constantly suffering from illness. I thought I would never recover, and I just wanted to deliver as soon as possible. Even I asked myself, what did we do? I was certain it would not pass. My health isn’t that bad, but it was quite bad.” (P2, female factor, 1-year infertility)

Theme 2: Overprotection

Overprotection is a natural reaction to the possibility of losing something that was so highly desired and hard-earned. When a mother experiences infertility, balancing motherhood and safeguarding pregnancy could be challenging and may lead to overprotection during pregnancy. In this study, this phenomenon is displayed in various aspects, including impacting changes in sexual life, delivery mode preferences, disruptions in social life, and an insistence on maintaining constant check-ups. It is imperative to recognize and address these feelings by healthcare providers to ensure a successful transition to pregnancy and a healthy psychological state.

Changes in Sexual Life

A pregnancy after struggling with infertility can significantly affect a couple’s sexual life. A shift in women’s sexual behaviors is often accompanied by a tendency to overprotection due to fears of potential complications.

“I don’t have a sexual life anymore. As soon as I learned I was pregnant, my husband would not even touch me.” (P4, female factor, 4-years infertility)
“I am afraid of causing harm to the baby, so we don’t want it at all. It is fear, not reluctance, that makes us avoid sexual activity.” (P2, female factor, 1-year infertility)

Delivery Mode Preference

Delivery mode preferences should be based on medical advice and individual circumstances. However, for couples who have experienced infertility, overprotection thoughts can influence their decision-making process. It is imperative to understand the reasons behind preferences and provide comprehensive information during the prenatal period. This will assist couples in making an informed decision.

“I am insistent on a cesarean section because I’m afraid something will happen during labor and harm the baby.” (P15, female factor, 10-years infertility)

Disrupted Social Life

Experiencing a pregnancy after infertility can change one’s social life. Transitioning from infertility to pregnancy can also introduce new dynamics and challenges in social interaction, while the entire experience can be isolating in itself.

“Pregnancy left me socially withdrawn.” (P13, female factor, 8-years infertility)
“Fear of abortion prevented us from having a normal social life during pregnancy. I always had to rest in bed. Consequently, we were socially isolated.” (P8, female factor, 4-years infertility)

Maintaining Constant Check-ups

Couples who experience pregnancy after long-term infertility often maintain continuous perinatal check-ups due to overprotective thoughts and a desire for early detection of potential complications. Anxiety and cautiousness can often increase among women who have struggled with infertility, fearing something unexpected may occur during pregnancy.

“In the first trimester, the doctor told me there might be something wrong with his heartbeat, so I tried to schedule an appointment with another doctor that day or sooner, but I couldn’t find an appointment, so I went to a private hospital right away.” (P2, female factor, 1-year infertility)
“My perinatal care and follow-ups are carried out in two private hospitals, a state hospital, a city hospital, and a university hospital. In other words, I visit for perinatal check-ups in five different hospitals to ease my anxiety and heightened concern. I also remembered that I visited different doctors three days in a row.” (P1, female factor, 1-year infertility)

Theme 3: Overthinking

For many couples struggling with infertility, overthinking during pregnancy is a common phenomenon. The long-term process of infertility treatment and its low success rate often results in considerable uncertainty, disappointment, and anxiety, which can continue to develop during pregnancy and cause mental overload.

In psychology, escapism is the act of removing oneself from issues, stress, or discomforts in the present moment. A number of effective strategies were used by the women in this study.

“I did a lot of knitting, quilting, and embroidery. I was always excited, and I needed to distract my thoughts.” (P15, female factor, 10-years infertility)

Information Hunting

In this study, the process of seeking knowledge, guidance, and reassurance during pregnancy after infertility is known as information hunting.

“During my pregnancy, I downloaded all available pregnancy programs, consulted friends who were pregnant or had delivery experiences, and consulted medical professionals” (P11, female & male factors, 7-years infertility)
“I did a lot of research on the internet about my pregnancy. I even burned my food because of it. My close friends also gave me advice….” (P6, female factor, 1-year infertility)

The Future in Mind

A woman who overcomes infertility and gets pregnant experiences different emotions. Eventually, her fears of losing the pregnancy turn into joy and hope. The mother began to dream about a future with the baby she had been expecting for so long.

“The baby’s coming makes us happy; our happiness depends on our baby. I am very happy and excited. At some point after all these years, I’d like to hear a baby crying at home. The scent of the baby is what I want to smell.” (P5, female factor, 2-years infertility)
“I want to cuddle my baby as soon as possible and put her to sleep with me. Spending time with my baby and going for walks together is what I dream of all the time.” (P4, female factor, 4-years infertility)

Whirlpool of Thought

In this study, the whirlpool of thought does not just refer to the feelings that mothers experience during pregnancy but also the overwhelming thoughts they deal with.

“As time went on, I got more and more worried, and the little things started to affect me very much.” (P7, Male factors, 2-years infertility)
“My priorities changed even while the fetus was still in my abdomen. I became more nervous, and I feared the baby would not move. Everything was planned around the baby.” (P11, female & male factors, 7-years infertility)

Theme 4: Social Activity Changes

For the expecting mother, pregnancy after infertility can cause a variety of social changes. During pregnancy and motherhood, she experiences profoundly personal and emotionally stressful moments.

Care Expectation

An expectant mother has unique expectations when it comes to pregnancy after infertility. During this precious time of infertility, the woman may have heightened emotional and physical needs.

“It would be great to have my mom and husband with me during pregnancy.” (P12, unexplained, 3-years infertility)
“In infertility and during pregnancy, you only need attention and thank God my husband was always by my side.” (P11, female & male factors, 7-years infertility)


An unconscious negative belief or feeling of a woman about her infertility process and pregnancy may lead to self-stigma at the time.

“I felt a sense of responsibility towards my husband since I was the one with the infertile factor and responsible for our infertility. I thought I wouldn’t have any chance of getting pregnant.” (P6, female factor, 1-year infertility)
“For a long time prior to becoming pregnant, I felt inadequate and useless in many aspects of my life. Our past difficulties during the infertility treatment process really exhaust me, I think.” (P13, female factor, 8-years infertility)

Spousal Support

Spouse support is imperative to navigating from infertility to a pregnancy process. In this study, most women reported their husbands supported them financially and emotionally during pregnancy.

“My husband liked me much better and started paying more attention to me after getting pregnant. Thank God, he helped me with house chores and didn’t make me tired doing them.” (P4, female factor, 4-years infertility)
“My husband is the most supportive person in my life. I may have talked a lot about him, but he really is my biggest supporter. I feel stronger when he is beside me; he is my source of strength.” (P1, female factor, 1-year infertility)

In this study, participants stated that they experience a variety of mixed emotions, which lead to a feeling of overwhelming following infertility treatment and during pregnancy. Our study identified four dominant themes: Overemotional Burden, Overprotection, Overthinking, and Social Activity Changes. Our results show that, pregnancy after long-term infertility treatments often results in roller-coaster emotions, fear, anxiety, and hopelessness in mothers. According to mothers’ reports, moments during pregnancy they caused mothers anxiety in different trimesters. They also mentioned feeling relieved and emotionally inspired by becoming mothers. The study conducted by Bovin et al. in 2023 showed that approximately 12% of women with infertility mentioned the words anxious and worried, and 8% mentioned disappointment as the most frequent words to describe their feelings even after pregnancy (Boivin et al., 2023 ). Swanson et al. in 2021 also describe the infertility process as full of a roller coaster of emotions and feelings. It is described that the infertility treatment process is characterized by a period of sadness and grief, followed by some renewed sense of hope after getting successful results (Swanson & Braverman, 2021 ).

According to our study, overprotection is reported as a common reaction among pregnant women during interviews. Fear of loss, a desire to safeguard, and long-awaited expectations of a baby can lead to overprotective attitudes, which are reflected in their sexual and social lives adversely and in the perinatal care process. It is also possible for such overprotection to impact the preferred mode of delivery to ensure the baby’s safety. In our study, mothers report decreasing or withdrawing from sexual activity during pregnancy due to fears that they may harm the baby, cause miscarriage, or adversely affect the pregnancy. Mothers also reported social isolation due to their fears of miscarriage or complications during pregnancy. According to our results, mothers need frequent medical check-ups and continuous monitoring to ensure their babies’ health and detect potential complications early to protect their fetuses. According to a study conducted by Fukui in 2021, mother-to-infant bonding scores were positively related to perceived maternal overprotective attitude in late pregnancy (Fukui et al., 2021 ). However, a study conducted by Ohara in 2018 showed that excessive overprotection sense during pregnancy could be determined as a cause of bonding failure during pregnancy. When a mother exhibits overprotective behavior during pregnancy, she generally spends more time in the caring process. As a result, she may not have enough time to establish a motherhood role during pregnancy (Ohara et al., 2018 ). A study conducted by Phan et al. in 2021 reported that approximately 30% of pregnant women had no sexual activity during their pregnancy. Pregnant women in this study were most concerned about infection and damage to the fetus (Phan et al., 2021 ).

In our study, pregnant women described adverse changes in their social life which introduce new dynamics and challenges to their social interaction. A study conducted in 2016 by Velikonja et al. reported that women who get pregnant with assisted reproductive technologies are more likely to be socially marginalized. They also reported greater positive emotions and greater life satisfaction as the pregnancy progressed despite experiencing more medical challenges during the pregnancy (Velikonja et al., 2016 ).

According to our results, mothers experiencing pregnancy following long-term infertility tend to choose delivery modes based on their overprotective attitudes. In 2015, Reichelt et al. reported that the Cesarean rate was significantly higher in pregnancy after infertility than in spontaneous pregnancy compared to 20 years ago (Reichelt et al., 2015 ). A study by Chien et al. in 2015 reported that infertility treatment significantly influences mothers’ decisions about delivery modes. They also found that women who get pregnant with the In vitro fertilization method (IVF) may consider their fetus to be more vulnerable, and a cesarean delivery may be considered a low-risk method for the baby’s safety (Chien et al., 2015 ).

Overthinking in this study highlights how women who experience infertility always struggle with pregnancy-related thoughts. During the infertility treatment period, women may experience uncertainty, disappointment, and anxiety Several times. This condition can even cause psychological overload and mental exhaustion during pregnancy. Mothers in our study mentioned doing handicrafts such as knitting as a way to escape overthinking. Pedro, in 2015, identified escapism as a successful strategy for avoiding overthinking regarding infertility, pregnancy, and delivery in infertile women. Infertile women in this study describe escapism as a way to avoid thinking about anything else except their activities (Pedro, 2015 ). In our study, mothers mentioned they are always in the middle of information hunting online, in person, or consulting resources, even for the most straightforward pregnancy-related questions. A study conducted by Brochu et al. in 2019 reported that approximately 90% of infertile individuals surf the internet for information about infertility treatment processes and attempt to find more mental and medical support (Brochu et al., 2019 ). We believe that overthinking during pregnancy can be calming and exciting, especially when dreaming about the baby. However, when it is exaggerated, it can lead to overwhelming stress and anxiety in mothers. Mothers in our study shared their most precious dreams of holding their new baby after a challenging infertility journey. In our study, it was observed that that most infertile couples think about their future with their expected baby at least once a day.

Pregnancy after infertility results in various social changes for women (Boulet et al., 2017 ). A woman’s emotional experience of infertility and her various concerns and experiences during pregnancy adversely affect her mental health and contribute to the stigma attached to infertility (Zargar et al., 2023 ). In our study, women with female factor infertility often suffer from self-stigma and low self-esteem. According to Lin et al. in 2020, women who experience infertility suffer from low self-confidence and feeling inadequate, which results in self-stigma (Lin et al., 2022 ). In this study, mothers describe spousal support as an essential support system to overcome infertility challenges. In addition, they reported a higher level of mental support following pregnancy, as well as assisting with household chores and other daily activities. According to Choi et al. in 2023, spousal support significantly reduces stress levels in women with infertility. Women with a supportive husband are more likely to develop a more positive attitude towards infertility treatment and can also cope more effectively with infertility challenges (Choi & Moon, 2023 ).


The study has limitations, despite providing valuable insight into women’s emotional challenges after infertility. The small sample size limits generalizability of results, since qualitative research inherently cannot be generalized to a broader population. Additionally, a stratified thematic analysis based on factors such as age group, education level, cause of infertility, and number of previous attempts was not feasible due to the high level of variability among the women included in the study. A more nuanced understanding of the diverse experiences would have been possible. A qualitative study involving spouses, family members, and healthcare professionals can provide valuable insight into the emotional challenges pregnant women face after infertility from multiple perspectives. A better understanding of the complexities of this experience could lead to more comprehensive support systems and interventions to help women who are experiencing these emotional challenges.

Pregnancy after infertility is a unique and emotionally charged experience for women, encompassing a spectrum of feelings that can be difficult to express. Our study results show four dominant themes which illustrate women’s complex emotional experiences and coping strategies during pregnancy. In this study, Over Emotional Burden, Overprotection, Overthinking, and Social Activity Changes were mentioned as the most common psychological concerns during pregnancy after infertility. Study results suggest healthcare providers who interact directly with pregnant women should be aware of these emotional burdens to understand mothers better and counsel them more effectively. Midwives, psychologists, and psychiatrists should also be more attentive to mothers’ emotions and provide coping mechanisms. This will enable mothers to experience their pregnancy with fewer possible adverse effects on both mother and fetus. However, research on infertile women’s emotional reactions and mental reactions during pregnancy is still limited. It may result from infertile mothers often concealing their emotions, ideas, and beliefs during pregnancy because they sacrifice themselves for the expected baby. This study can provide a starting point for further qualitative and quantitative studies on the psychological burden of pregnant women who experience infertility for a long time.

Data availability

The data supporting the findings of this study are available upon reasonable request.

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We sincerely thank the participants for sharing with us all of their experiences, no matter how positive or negative, hoping that no one will suffer as they did.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Parisa Hadavibavili & Yasemin Hamlaci Başkaya

Institute of Health Science, Department of Midwifery, Sakarya University, Sakarya, Türkiye

Parisa Hadavibavili

Training and Research Hospital, Sakarya University, Sakarya, Türkiye

Gamze Bayazi̇t

Department of Obstetrics and Gynaecology, Training and Research Hospital, Sakarya University, Sakarya, Türkiye

Arif Serhan Cevrioğlu

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Hadavibavili, P., Hamlaci Başkaya, Y., Bayazi̇t, G. et al. Women’s emotional roller coasters during pregnancy as a consequence of infertility: a qualitative phenomenological study. Curr Psychol (2024). https://doi.org/10.1007/s12144-024-06158-3

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  • Infertility (MeSH D007246)
  • Pregnancy (MeSH D011247)
  • Qualitative research (MeSH D036301)
  • Mental health (MeSH D008603)
  • Fertility (MeSH D005298)
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