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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

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

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

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

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

Focus Groups

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

Ethnographic Studies

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

Text Analysis

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

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

Process of Observation

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

Record Keeping

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

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

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

Qualitative Research Analysis Methods

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

Thematic Analysis

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

Content Analysis

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

Discourse Analysis

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

Grounded Theory Analysis

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

Narrative Analysis

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

Phenomenological Analysis

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

Comparative Analysis

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

Applications of Qualitative Research

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

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

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

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

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

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

Purpose of Qualitative Research

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

Qualitative research can serve multiple purposes, including:

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

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

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

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

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

Advantages of Qualitative Research

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

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

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

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

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Qualitative Research: Goals, Methods & Benefits

By Jim Frost 5 Comments

Qualitative research aims to understand ideas, experiences, and opinions using non-numeric data, such as text, audio, and visual recordings. The focus is on language, behaviors, and social structures. Qualitative researchers want to present personal experiences and produce narrative stories that use natural language to provide meaningful answers to their research questions.

Qualitative research focuses on descriptions, opinions, and experiences rather than numbers. Standard data collection techniques include interviews, diaries, focus groups, documents, artifacts, and direct observations.

Qualitative research provides a sharp contrast to quantitative research, which uses numeric data and statistical analyses to understand a concrete reality. The vast majority of content on my website is about quantitative research and statistical analyses. However, there are areas where qualitative research is more effective at understanding dynamic social structures and subjective perceptions in a real-world that can be convoluted.

Psychologists created qualitative research because the traditional methods failed to understand the human experience. Consequently, they developed a naturalistic approach that focuses on human behavior, what gives people meaning, how they perceive things, and why they act in a particular manner. This process involves understanding the people in their natural settings and social interactions.

Psychology, sociology, anthropology, education, and history frequently use qualitative research. Marketing groups also use it to understand how real people use their products, what factors increase usage, and obstacles that reduce usage. Ultimately, they want to market their products better, which requires understanding consumer mindsets.

Examples of Qualitative Research Questions

Qualitative research can answer a wide range of questions. Below are six example research questions.

• What factors shape body image?
• How do single-parent homes affect children?
• What challenges do consumers face in adopting a company’s new product?
• How does social media affect anxiety?
• What effect does previous domestic violence have on current relationships?
• What are the unique problems that night shift workers face?

Learn how to create research questions for scientific studies .

Qualitative Research Methods

Ethnography

The researchers embed themselves in the daily lives of their subjects and their social groups. Their goal is to understand their habits, routines, beliefs, and challenges.

For an excellent guide to observing participants in the field, read Qualitative Research Methods: A Data Collector’s Field Guide [external PDF].

Narrative Research

An alternative qualitative approach is to interview several subjects in-depth, gather documents, and collect artifacts. The researchers then piece these multiple lines of evidence together to create a narrative that answers the research question.

Phenomenology

Qualitative researchers can study an event as it happens from different vantage points. For instance, they can conduct interviews, record videos, and directly observe the proceedings to understand the participants’ subjective experiences.

Grounded Theory

This form of qualitative research differs from most other methods. The researchers start with a qualitative dataset and then sort through these data, tagging concepts and ideas. As the study continues, they organize and group the conceptual tags. During this process, the researchers watch for hypotheses to emerge. This method seeks to let the scientists organically react to the dataset but yet ground the results in as much empirical data as possible.

Case Studies

A case study usually examines one subject in great detail. The subject can be a person, business, or other organization. The goal is to understand the subject as much as possible and use that information to understand the larger population to some extent. This qualitative research method can foster understanding of the motivations, influences, and factors that lead to success or failure. Learn more about What is a Case Study? Definition & Examples .

Qualitative Research Data Collection Methods

Below are the standard data collection methods for qualitative research. Studies can combine multiple methods.

• Secondary research : Use existing documents, photographs, audio, and video.
• Interviews : One-on-one guided conversations.
• Direct observations : Researchers observe the subjects in the field and take notes.
• Questionnaires : Qualitative research frequently uses surveys with open-ended questions.
• Focus groups : A guided small group conversation where the discussion provides the data.

Analyzing Qualitative Data

After collecting their data, qualitative researchers have multiple ways to analyze the content. A common approach is to add codes that represent meaningful ideas to communications, documents, videos, etc. The researchers evaluate frequencies and patterns of these conceptual codes. They can also find the most common words, thematic patterns, communications structure, and the method by which communications obtain specific goals. Analysts refer to these approaches with names such as content analysis, thematic analysis, textual analysis, etc.

Advantages and Disadvantages of Qualitative Research

Qualitative research has many advantages because it seeks to record the subjects’ lived experiences and understand them in ways that quantitative data cannot. Going beyond just the numbers, they can gain insights into opinions, emotions, and perceptions. These studies frequently occur in natural environments and real-world social contexts rather than labs and other artificial environments that might affect the participants, particularly when talking about personal matters.

Unlike quantitative research, qualitative methods are flexible. Researchers can change their methodology and theories as they gather information. The open-ended nature of qualitative research allows the researchers to uncover new ideas they hadn’t anticipated and adjust accordingly.

However, qualitative research has some disadvantages.

Its primary disadvantage is that it is more subjective than quantitative research. It’s harder to separate the researchers’ opinions and predilections from the more personal nature of qualitative data. Determining what concepts to code and when to apply those codes can be highly subjective. Flexibly adapting the research on the fly can be great, but it also increases the prominence of the researcher’s personal determination of relevance.

Furthermore, consider how ordinary people can observe the same reality in all its real-world messiness and draw different conclusions. Similarly, qualitative researchers can evaluate the same real-world data and produce dissimilar findings.

Qualitative research typically uses small samples that are less likely to be representative , which limits generalizability . Finally, as with other types of observational studies , the real-world settings in qualitative research can be an advantage, but they potentially introduce a host of confounding variables that can bias the results.

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Reader Interactions

August 1, 2023 at 10:42 am

If qualitative data is counted in categorical, ordinal, or binary forms does it become quantitative data?

January 2, 2023 at 11:27 am

Who are the actual people at the foundations of qualitative research as we know it? We know they are generally psychologists, like creswell who seems to have updated a but for the modern era, but who stands out the most in research throughout the age of qualitative research?

November 22, 2022 at 11:04 am

Have you publish on qualitative methods and surveys?

November 22, 2022 at 4:19 pm

I haven’t as of yet. Probably down the road, particularly for surveys.

April 23, 2022 at 2:16 pm

Can regression results from another study be used for my data collection, as a form of secondary data? I believe that the regression results are important to my study, but I don’t know if “results” from another study, specifically taken from their appendix table can be pasted into my “data collection section” of my research paper. I wish to employ a grounded theory research methodology that is mixed methods in approach, because I can apply regression analysis to the regression results, but I question the possibility of doing this for my data collection section.

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Qualitative Methods and Analysis

• First Online: 24 March 2020

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The main qualitative research methods are discussed and compared. Data analysis, writing up results, and presenting findings are covered. The principal qualitative approaches explored in this chapter are interviews and qualitative questionnaires. These methods seek meanings, understandings and interpretations from individuals speaking directly to a researcher. Also discussed are case studies, action research, observation, content analysis and systematic reviews. A case study is a multi-method approach used when a specific instance or situation is chosen to investigate a general phenomenon. In action research, researchers and participants work together. In both cases, and in particular in action research, the goal is to improve a particular situation or service. Part of both of the latter methods may involve observations, where researchers look at what is taking place ‘live’ in situ. They may also undertake a content analysis, which is a systematic analysis of records, documents, and field notes. Removed completely from a fieldwork setting, the final method discussed in this chapter is the systematic review. This aims to rigorously evaluate and synthesise findings of prior primary research.

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Williams, P., Cutler, S. (2020). Qualitative Methods and Analysis. In: Ramlaul, A. (eds) Medical Imaging and Radiotherapy Research: Skills and Strategies. Springer, Cham. https://doi.org/10.1007/978-3-030-37944-5_16

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Article contents

Qualitative data analysis and the use of theory.

• Carol Grbich Carol Grbich Flinders University
• https://doi.org/10.1093/acrefore/9780190264093.013.554
• Published online: 23 May 2019

The role of theory in qualitative data analysis is continually shifting and offers researchers many choices. The dynamic and inclusive nature of qualitative research has encouraged the entry of a number of interested disciplines into the field. These discipline groups have introduced new theoretical practices that have influenced and diversified methodological approaches. To add to these, broader shifts in chronological theoretical orientations in qualitative research can be seen in the four waves of paradigmatic change; the first wave showed a developing concern with the limitations of researcher objectivity, and empirical observation of evidence based data, leading to the second wave with its focus on realities - mutually constructed by researcher and researched, participant subjectivity, and the remedying of societal inequalities and mal-distributed power. The third wave was prompted by the advent of Postmodernism and Post- structuralism with their emphasis on chaos, complexity, intertextuality and multiple realities; and most recently the fourth wave brought a focus on visual images, performance, both an active researcher and an interactive audience, and the crossing of the theoretical divide between social science and classical physics. The methods and methodological changes, which have evolved from these paradigm shifts, can be seen to have followed a similar pattern of change. The researcher now has multiple paradigms, co-methodologies, diverse methods and a variety of theoretical choices, to consider. This continuum of change has shifted the field of qualitative research dramatically from limited choices to multiple options, requiring clarification of researcher decisions and transparency of process. However, there still remains the difficult question of the role that theory will now play in such a high level of complex design and critical researcher reflexivity.

• qualitative research
• data analysis
• methodologies

Theory and Qualitative Data Analysis

Researchers new to qualitative research, and particularly those coming from the quantitative tradition, have often expressed frustration at the need for what appears to be an additional and perhaps unnecessary process—that of the theoretical interpretation of their carefully designed, collected, and analyzed data. The justifications for this process have tended to fall into one of two areas: the need to lift data to a broader interpretation beyond the Monty Pythonesque “this is my theory and it’s my very own,” to illumination of findings from another perspective—by placing the data in its relevant discipline field for comparison with previous theoretical data interpretations, while possibly adding something original to the field.

“Theory” is broadly seen as a set of assumptions or propositions, developed from observation or investigation of perceived realties, that attempt to provide an explanation of relationships or phenomena. The framing of data via theoretical imposition can occur at different levels. At the lowest level, various concepts such as “role,” “power,” “socialization,” “evaluation,” or “learning styles” refer to limited aspects of social organization and are usually applied to a specific group of people.

At a more complex level, theories of the Middle Range, identified by Robert Merton to link theory and practice, are used to build theory from empirical data. These tend to be discipline specific and incorporate concepts plus variables such as “gender,” “race,” or “class.” Concepts and variables are then combined into meaningful statements, which can be applied to more diverse social groups. For example, in education an investigation of student performance could emphasize such concepts as “safety,” “zero bullying,” “communication,” and “tolerance,” with variables such as “race” and “gender” to lead to a statement that good microsystems and a focus on individual needs are necessary for optimal student performance.

The third and most complex level uses the established or grand theories such as those of Sigmund Freud’s stages of children’s development, Jean Piaget’s theory of cognitive development, or Urie Bronfenbrenner’s ecological systems, which have been widely accepted as meaningful across a number of disciplines and provide abstract explanations of the uniformity of aspects of social organization, social behavior, and social change.

The trend in qualitative research regarding the application of chosen levels of theory has been generally either toward theory direction/verification or theory generation, although the two are often intertwined. In the first, a relevant existing theory is chosen early and acts as a point of critical comparison for the data to be collected. This approach requires the researcher to think theoretically as s/he designs the study, collects data, and collates it into analytical groupings. The danger of theory direction is that an over focus on a chosen theoretical orientation may limit what the researcher can access or “see” in the data, but on the upside, this approach can also enable the generation of new theoretical aspects, as it is rare that findings will fall precisely within the implications of existing statements. Theory generation is a much looser approach and involves either one or a range of relevant levels of theory being identified at any point in the research process, and from which, in conjunction with data findings, some new combination or distillation can enhance interpretation.

The question of whether a well-designed study should negate the need for theoretical interpretation has been minimally debated. Mehdi and Mansor ( 2010 ) identified three trends in the literature on this topic: that theory in qualitative research relates to integrated methodology and epistemology; that theory is a separate and additional element to any methodological underpinnings; and that theory has no solid relationship with qualitative research. No clear agreement on any of these is evident. Overall, there appears to be general acceptance that the process of using theory, albeit etically (imposed) or emically (integrated), enhances outcomes, and moves research away from being a-theoretical or unilluminated by other ideas. However, regarding praxis, a closer look at the issue of the use of theory and data may be in order. Theoretical interpretation, as currently practiced, has limits. To begin with, the playing field is not level. In the grounded theory tradition, Glaser and Strauss ( 1967 ) were initially clear that in order to prevent undue influence on design and interpretation, the researcher should avoid reviewing the literature on a topic until after some data collection and analysis had been undertaken. The presumption that most researchers would already be well versed in theory/ies and would have a broad spectrum to draw on in order to facilitate the constant comparative process from which data-based concepts could be generated was found to be incorrect. Glaser ( 1978 ) suggested this lack could be improved at the conceptual level via personal and professional reflexivity.

This issue became even more of a problem with the advent of practice-led disciplines such as education and health into the field of qualitative research. These groups had not been widely exposed to the theories of the traditional social sciences such as sociology, psychology, and philosophy, although in education they would have been familiar with John Dewey’s concept of “pragmatism” linking learning with hands-on activity, and were more used to developing and using models of practice for comparison with current realities. By the mid- 20th century , Education was more established in research and had moved toward the use of middle range theories and the late 20th-century grand theorists: Michel Foucault, with his emphasis on power and knowledge control, and Jurgen Habermas, with his focus on pragmatism, communication, and knowledge management.

In addition to addictive identification with particular levels of theory and discipline-preferred theories and methods, activity across qualitative research seems to fall between two extremes. At one end it involves separate processes of data collection and analysis before searching for a theoretical framework within which to discuss the findings—often choosing a framework that has gained traction in a specific discipline. This “best/most acceptable fit” approach often adds little to the relevant field beyond repetition and appears somewhat forced. At the other extreme there are those who weave methods, methodologies, data, and theory throughout the whole research process, actively critiquing and modifying it as they go, usually with the outcome of creating some new direction for both theory and practice. The majority of qualitative research practice, however, tends to fall somewhere between these two.

The final aspect of framing data lies in the impact of researchers themselves, and the early- 21st-century emphasis is on exposing relevant personal frames, particularly those of culture, gender, socioeconomic class, life experiences such as education, work, and socialization, and the researcher’s own values and beliefs. The twin purposes of this exposure are to create researcher awareness and encourage accountability for their impact on the data, as well as allowing the reader to assess the value of research outcomes in terms of potential researcher bias or prejudice. This critical reflexivity is supposed to be undertaken at all stages of the research but it is not always clear that it has occurred.

Paradigms: From Interactionism to Performativity

It appears that there are potentially five sources of theory: that which is generally available and can be sourced from different disciplines; that which is imbedded in the chosen paradigm/s; that which underpins particular methodologies; that which the researcher brings, and that which the researched incorporate within their stories. Of these, the paradigm/s chosen are probably the most influential in terms of researcher position and design. The variety of the sets of assumptions, beliefs, and researcher practices that comprise the theoretical paradigms, perspectives, or broad world views available to researchers, and within which they are expected to locate their individual position and their research approach, has shifted dramatically since the 1930s. The changes have been distinct and identifiable, with their roots located in the societal shifts prompted by political, social, and economic change.

The First Wave

The Positivist paradigm dominated research, largely unquestioned, prior to the early 20th century . It emphasized the distancing of the researcher from his/her subjects; researcher objectivity; a focus on objective, cause–effect, evidence-based data derived from empirical observation of external realities; experimental quantitative methods involving testing hypotheses; and the provision of finite answers and unassailable future predictions. From the 1930s, concerns about the limitations of findings and the veracity of research outcomes, together with improved communication and exposure to the worldviews of other cultures, led to the advent of the realist/post-positivist paradigm. Post-positivism, or critical realism, recognized that certainty in proving the truth of a hypothesis was unachievable and that outcomes were probably limited to falsification (Popper, 1963 ), that true objectivity was unattainable and that the researcher was most likely to impact on or to contaminate data, that both qualitative and quantitative approaches were valuable, and that methodological pluralism was desirable.

The Second Wave

Alongside the worldwide political shifts toward “people power” in the 1960s and 1970s, two other paradigms emerged. The first, the Interpretivist/Constructivist, focused on the social situations in which we as humans develop and how our construction of knowledge occurs through interactions with others in these contexts. This paradigm also emphasized the gaining of an understanding of the subjective views or experiences of the participants being researched, and recognized the impact of the researcher on researcher–researched mutually constructed realities. Here, theory generation is the preferred outcome to explain the what, how, and why of the findings. This usually involves the development of a conceptual model, forged from both the data gained and from the application/integration of relevant theory, to provide explanations for and interpretations of findings, together with a new perspective for the field/discipline.

The second paradigm, termed the Critical/Emancipatory, focused on locating, critiquing, and changing inequalities in society. The identification of the location of systemic power discrepancies or systematic power misuse in situations involving gender, sexuality, class, and race is expected to be followed by moves to right any oppression discovered. Here, the use of theory has been focused more on predetermined concept application for “fit.” This is because the very strong notion of problematic societal structures and power inappropriately wielded have been the dominant underpinnings.

In both the Interpretive and Critical paradigms, researcher position shifted from the elevated and distant position of positivism, to one of becoming equal with those being researched, and the notion of researcher framing emerged to cover this shift and help us—the readers—to “see” (and judge) the researcher and her/his processes of data management more clearly.

The Third Wave

In the 1980s, the next wave of paradigmatic options—postmodernism and poststructuralism—emerged. Postmodernism, with its overarching cultural implications, and poststructuralism, with its focus on language, severely challenged the construction, limitations, and claims to veracity of all knowledge and in particular the use of theory derived from siloed disciplines and confined research methods. Regardless of whether the postmodern/poststructural label is attached to grounded theory, ethnography, phenomenology, action, or evaluative designs, one general aspect that prevails is a focus on language. Language has become viewed as dubious, with notions of “slippage”—the multiple meanings of individual words, and “difference”—the difference and deferral of textual meaning (Derrida, 1970 , 1972 ), adding complexity. Double coding, irony, and juxtaposition are encouraged to further identify meaning, and to uncover aspects of social organization and behavior that have been previously marginalized or made invisible by existing discourses and discursive practices. Texts are seen as complex constructions, and intertextuality is favored, resulting in multiply constructed texts. The world is viewed as chaotic and unknowable; individuals are no longer seen as two dimensional—they are viewed as multifaceted with multiple realities. Complex “truths” are perceived as limited by time and context, requiring multiple data sets and many voices to illuminate them, and small-scale focused local research is seen as desirable. The role of researcher also changed: the politics of position and self-reflexivity dominate and the researcher needs to clearly expose past influences and formerly hidden aspects of his/her life. S/he inhabits the position of an offstage or decentered facilitator, presenting data for the reader to judge.

Theory is used mainly at the conceptual level with no particular approach being privileged. The researcher has become a “bricoleur” (Levi-Strauss, 1962 ) or handyman, using whatever methods or theories that are within reach, to adapt, craft, and meld technological skills with mythical intellectual reflection in order to create unique perspectives on the topic. Transitional interpretations dominate, awaiting further challenges and deconstruction by the next researcher in the field.

The need for multifaceted data sets in the 1990s led inevitably to a search for other research structures, and mixed and multiple methods have become topical. In crossing the divide between qualitative and quantitative approaches, the former initially developed its own sub-paradigms: pragmatist (complimentary communication and shared meanings) and transformative/emancipatory (inequalities in race, class, gender, and disability, to be righted). An increasing focus on multiple methods led to the advent of dialectics (multiple paradigm use) and critical realism (the acceptance of divergent results) (Shannon-Baker, 2016 ). The dilemmas of theory use raised by these changes include whether to segregate data sets and try to explain disparate outcomes in terms of diversity using different theories; whether to integrate them through a homogeneous “smoothing” process—one theory fits all, in order to promote a singular interpretation; or whether to let the strongest paradigm—in terms of data—dominate the theoretical findings.

The Fourth Wave

During the early 21st century , as the third wave was becoming firmly established, the Performative paradigm emerged. The incorporation of fine art–based courses into universities has challenged the prescribed rules of the doctoral thesis, initially resulting in a debate—with echoes of Glaser and Strauss—as to whether theory, if used initially, is too directive, thereby potentially contaminating the performance, or whether theory application should be an outcome to enhance performances, or even whether academic guidelines regarding theory use need to be changed to accommodate these disciplines (Bolt, 2004 ; Freeman, 2010 ; Riley & Hunter, 2009 ). Performativity is seen in terms of “effect,” a notion derived from John Austin’s ( 1962 ) assertion that words and speech utterances do not just act as descriptors of content, they have social force and impact on reality. Following this, a productive work is seen as capable of transforming reality (Bolt, 2016 ). The issue most heard here is the problem of how to judge this form of research when traditional guidelines of dependability, transformability, and trustworthiness appear to be irrelevant. Barbara Bolt suggests that drawing on Austin’s ( 1962 ) terms “locutionary” (semantic meaning), “illocutionary” (force), and “perlocutionary” (effect achieved on receivers), together with the mapping of these effects in material, effective, and discursive domains, may be useful, despite the fact that mapping transformation may be difficult to track in the short term.

During the second decade of the 21st century , however, discussions relating to the use of theory have increased dramatically in academic performative research and a variety of theoreticians are now cited apart from John Austin. These include Maurice Merleu-Ponty ( 1945 and the spatiality of lived events; Jacques Derrida ( 1982 ) on iterability, simultaneous sameness, and difference; Giles Deleuze and Felix Guatarri ( 1987 ) on rituals of material objects and transformative potential; Jean-Francois Lyotard ( 1988 ) on plurality of micro narratives, “affect,” and its silent disruption of discourse; and Bruno Latour ( 2005 ) with regard to actor network theory—where theory is used to engage with rather than to explain the world in a reflective political manner.

In performative doctoral theses, qualitative theory and methods are being creatively challenged. For example, from the discipline of theater and performance Lee Miller and Joanne/Bob Whalley ( 2010 ) disrupt the notion of usual spaces for sincere events by taking their six-hour-long performance Partly Cloudy, Chance of Rain , involving a public reaffirmation of their marriage vows, out of the usual habitats to a service station on a highway. The performance involves a choir, a band, a pianist, 20 performers dressed as brides and grooms, photographers, a TV crew, an Anglican priest, plus 50 guests. The theories applied to this event include an exploration of Marc Auge’s ( 1992 ) conception of the “non-place”; Mikhail Bakhtin’s ( 1992 ) concepts of “dialogism” (many voices) together with “heteroglossia” (juxtaposition of many voices in a dialogue); and Ludwig Wittgenstein’s ( 1953 ) discussion of the “duck rabbit”—once the rabbit is seen (participatory experience) the duck (audience) is always infected by its presence. This couple further challenged the guidelines of traditional doctoral theses by successfully negotiating two doctoral awards for a joint piece of research

A more formal example of a doctoral thesis (Reik, 2014 ) using traditional qualitative approaches has examined at school level the clash of paradigms of performative creative styles of teaching with the neoliberalist focus on testing, curriculum standardization, and student outcomes.

Leah Mercer ( 2012 ), an academic in performative studies, used the performative paradigm in her doctoral thesis to challenge and breach not only the methodological but also the theoretical silos of the quantitative–qualitative divide. The physics project is an original work using live performances of personal storytelling with video and web streaming to depict the memories, preoccupations, and the formative relationship of two women, an Australian and an American, living in contemporary mediatized society. Using scientific theory, Mercer explores personal identity by reframing the principles of contemporary physics (quantum mechanics and uncertainty principle) as aesthetic principles (uncertainty and light) with the physics of space (self), time (memory), light (inspiration), and complementarity (the reconciliation of opposites) to illuminate these experiences.

The performative paradigm has also shifted the focus on the reader, developed in postmodernism, to a broader group—an active audience. Multi-methods have been increased to include symbolic imagery, in particular visual images, as well as sound and live action. The researcher’s role here is often that of performer within a cultural frame, creating and investigating multiple realities and providing the link between the text/script and the audience/public. Theory is either minimized to the level of concepts or used to break through the silos of different disciplines to integrate and reconcile aspects from long-lasting theoretical divides.

In these chronological lines of paradigm shifts, changes in researcher position and changes in the application of theory can clearly be seen. The researcher has moved out of the shadows and into the mainstream; her/his role has shifted from an authoritarian collector and presenter of finite “truths” to a creator and often performer of multiple and disparate data images for the audience to respond to. Theory options have shifted from direction and generation within existing perspectives to creative amalgamations of concepts from disciplines previously rarely combined.

Methodologies: From Anthropology to Fine Arts

It would be a simple matter if all the researcher had to contend with was siting oneself in a particular paradigm/s. Unfortunately, not only have paradigms shifted in terms of researcher position and theoretical usage but so also have methodological choices and research design. One of the most popular methodologies, ethnography, with its roots in classical anthropology and its fieldwork-based observations of action and interaction in cultural contexts, can illustrate the process of methodological change following paradigm shift. If a researcher indicates that he/she has undertaken an ethnographic study, the reader will be most likely to query “which form?”: classical?, critical?, auto?, visual?, ethno drama?, cyber/net?, or performative? The following examples from this methodology should indicate how paradigm shifts have resulted in increasing complexity of design, methods, and interpretive options.

In c lassical ethnography the greatest borrowing is from traditional anthropology in terms of process and tools, and this can be seen with the inclusion of initial time spent in the setting to learn the language of the culture and to generally “bathe” oneself in the environment, often with minimal data collection. This process is supposed to help increase researcher understanding of the culture and minimize the problem of “othering” (treating as a different species/alien). Then a fairly lengthy amount of time is usually spent in the cultural setting either as an observer or as a participant observer to collect as much data as is relevant to answer the research question. This is followed by a return to post-check whether the findings previously gathered have stood the test of time. The analytical toolkit can involve domain analysis, freelists, pilesorts, triads and taxonomies, frame and social network, and event analysis. Truncated mini-ethnographies became more common as time became an issue, but these can still involve years of managing descriptive data, often collected by several participating researchers as seen in Douglas, Rasmussen, and Flanagan’s ( 1977 ) study of the culture of a nudist beach. Shorter versions undertaken by one researcher, for example Sohn ( 2015 ), have explored strategies of teacher and student learning in a science classroom. Theoretical interpretation can be by conceptual application for testing, such as Margaret Mead’s ( 1931 ) testing of the concept of “adolescence”—derived from American culture—in Samoan culture, or, more generally, by concept generation. The latter can be seen in David Rozenhan’s ( 1973 ) investigation of the experience of a group of researcher pseudo-patients admitted to hospitals for the mentally ill in the United States. The main concepts generated were labeling, powerlessness, and depersonalization.

De-colonial ethnography recognizes the “othering” frames of colonial and postcolonial research and takes a position that past colonial supremacy over Third World countries persists in political, economic, educational, and social constructions. Decolonizing requires a critical examination of language, attitudes, and research methods. Kakal Battacharya ( 2016 ) has exposed the micro-discourses of the continuing manifestation of colonial power in a parallel narrative written by a South Asian woman and a white American male. Concepts of colonialism and patriarchy, displayed through the discourses exposed, provide a theoretical critique.

Within critical ethnography , with its focus on power location and alleviation of oppression, Dale Spender ( 1980 ) used structured and timed observations of the styles, quality, and quantity of interaction between staff and students in a range of English classrooms. The theory-directive methodological frames of feminism and gender inequality were applied to identify and expose the lesser time and lesser quality of interaction that teachers had with female students in comparison with that assigned to male students. Widespread distribution of these results alerted education authorities and led to change, in some environments, toward introducing single-sex classrooms for certain topics. This was seen as progress toward alleviating oppressive behaviors. This approach has produced many excellent educational studies, including Peter Willis ( 1977 ) on the preparation of working-class kids for working-class jobs; Michele Fine ( 1991 ) on African American and Latino students who dropped out of a New York high school; Angela Valenzuela ( 1999 ) on emigrant and other under-achievers in American schools; Lisa Patel ( 2013 ) on inclusion and exclusion of immigrants into education; and Jean Anyon ( 1981 ) on social stratification of identical curriculum knowledge in different classrooms

A less concept-driven and more descriptive approach to critical ethnography was emphasized by Phil Carspecken’s hermeneutic approach ( 1996 ), which triggered a move toward data-generated theoretical concepts that could then be used to challenge mainstream theoretical positions.

Post-critical ethnography emphasizes power and ideology and the social practices that contribute to oppression, in particular objectivity, positionality, representation and reflexivity, and critical insufficiency or “antipower.”

Responsibility is shifted to the researcher for the world they create and critique when they interpret their research contexts (Noblit, Flores, & Murillo, 2004 ).

Autoethnography emerged from the postmodern paradigm, with its search for different “truths” and different relationships with readers, and prompted an emphasis on personal experience and documentation of the self in a particular cultural context (Ellis, 2004 ). In order to achieve this, the researcher has to inhabit the dual positions of being the focus of activities, feelings, and emotions experienced in the setting while at the same time being positioned distantly—observing and recording the behaviors of the self in that culture. Well-developed skills of critical reflexivity are required. The rejection of the power-laden discourses/grand theories of the past and the emphasis on transitional explanations has resulted in minimal theorizing and an emphasis on data display, the reader, and the reader’s response. Open presentations of data can be seen in the form of narrative storytelling, or re-presentations in the form of fiction, dramatic performances, and poetry. Carolyn Ellis ( 2004 ) has argued that “story is theory and theory is story” and our “making sense of stories” involves contributing to a broader understanding of human existence. Application/generation of concepts may also occur, and the term “Critical Autoethnography” has been used (Hughes & Pennington, 2017 ), particularly where experiences of race, class, or gender inequality are being experienced. Jennifer Potter ( 2015 ) used the concept “whiteness of silence” to introduce a critical race element into her autoethnographic experience of black–white racial hatred experiences within a university class on African American communication in which she was a student.

Visual ethnography uses a variety of tools, including photography, sketches, movies, social media, the Web and virtual reality, body art, clothing, painting, and sculpture, to demonstrate and track culture. This approach has been available for some time both as a methodology in its own right and as a method of data collection. An example of this approach, which mixes classical and visual ethnography, is Philippe Bourgois and Jeff Schonberg’s 12-year study of two dozen homeless heroin injectors and crack smokers living under a freeway overpass in San Francisco ( 2009 ). Their data comprised extensive black and white photos, dialogue, taped conversations, and fieldwork observation notes. The themes of violence, race relations, family trauma, power relations, and suffering were theoretically interpreted through reworked notions of “power” that incorporated Pierre Bourdieu’s ( 1977 , 1999 ) concepts of “symbolic violence”—linking observed practices to social domination, and “habitus”—an individual’s personal disposition comprising unique feelings and actions grounded in biography and history; Karl Marx’s “lumpen” from “lumpenproletariat” ( 1848 ), the residual class—the vagrants and beggars together with criminal elements that lie beneath the labor force; and Michel Foucault’s “biopower” ( 1978 , 2008 )—the techniques of subjugation used by the state on the population, and “governmentality” ( 1991 )—where individuals are disciplined through institutions and the “knowledge–power” nexus. The ideas of these three theorists were used to create and weave a theory of “lumpen abuse” to interpret the lives of the participants.

Ethno Drama involves transforming the results from an ethnographic study into a performance to be shared, for example the educational experiences of children and youth (Gabriel & Lester, 2013 ). The performance medium can vary from a film (Woo, 2008 ), an article presented in dramatic form (Carter, 2014 ), or more usually a play script to be staged for an audience in a theater (Ethno Theater). One of the main purposes is to provide a hearing space for voices that have been marginalized or previously silenced. These voices and their contexts can be presented by research participants, actors, or the research team, and are often directed at professionals from the field. Audience-based meetings to devise recommendations for further action may follow a performance. Because of the focus on inequality, critical theory has been the major theoretical orientation for this approach. The structure of the presentation invites audiences to identify situations of oppression, in the hope that this will inform them sufficiently to enable modification of their own practices or to be part of the development of recommendations for future change.

Lesnick and Humphrie ( 2018 ) explored the views of identity of LGBTQ+ youth between 14 and 24 years of age via interviews and online questionnaires, the transcriptions of which were woven into a script that was performed by actors presenting stories not congruent with their own racial/gender scripts in order to challenge audience expectations and labels. The research group encouraged the schools where they performed to structure discussion groups to follow the school-located performances. The scripts and discussions revealed and were lightly interpreted through concepts of homelessness, racism, and “oppression Olympics”—the way oppressed people sometimes view one another in competition rather than in solidarity. These issues were found to be relevant to both school and online communities. Support for these young people was discovered to be mostly from virtual sources, being provided by dialogues within Facebook groups.

Cyber/net or/virtual ethnographies involve the study of online communities within particular cultures. Problems which have emerged from the practice of this approach include; discovery of the researcher lurking without permission on sites, gaining prior permission which often disturbs the threads of interaction, gaining permission post–data collection but having many furious people decline participation, the “facelessness” of individuals who may have uncheckable multiple personas, and trying to make sense of very disparate data in incomplete and non-chronological order.. There has been acceptance that online and offline situations can influence each other. Dibbell ( 1993 ) demonstrated that online sexual violence toward another user’s avatar in a text-based “living room” reduced the violated person to tears as she posted pleas for the violator to be removed from the site. Theoretical interpretation at the conceptual level is common; Michel Foucault’s concept of heterotopia ( 1967 , 1984 ) was used to explain such spatio-temporal prisons as online rooms. Heterotropic spaces are seen as having the capacity to reflect and distort real and imagined experiences.

Poststructural ethnography tracks the instability of concepts both culturally and linguistically. This can be demonstrated in the deconstruction of language in education (Lather, 2001 ), particularly the contradictions and paradoxes of sexism, gender, and racism both in texts and in the classroom. These discourses are implicated in relations of power that are dynamic and within which resistance can be observed. Poststructuralism accepts that texts are multiple, as are the personas of those who created them, and that talk such as that which occurs in a classroom can be linked with knowledge control. Walter Humes ( 2000 ) discovered that the educational management discourses of “community,” “leadership,” and “participation” could be disguised by such terms as “learning communities” and “transformational leadership.” He analyzed the results with a conceptual framework derived from management theory and policy studies and linked the findings with political power.

Performative ethnography , from the post-postmodern paradigm, integrates the performances of art and theater with the focus on culture of ethnography (Denzin, 2003 ). A collaborative performance ethnography (van Katwyk & Seko, 2017 ) used a poem re-presenting themes from a previous research study on youth self-harming to form the basis of the creation of a performative dance piece. This process enabled the researcher participants to explore less dominant ways of knowing through co-learning and through the discovery of self-vulnerability. The research was driven by a social justice-derived concern that Foucault’s notion of “sovereignty” was being implemented through a web of relations that commodified and limited knowledge, and sanctioned the exploitation of individuals and communities.

This exploration of the diversity in ethnographic methods, methodologies, and interpretive strategies would be repeated in a similar trek through the interpretive, critical, postmodern, and post-postmodern approaches currently available for undertaking the various versions of grounded theory, phenomenology, feminist research, evaluation, action, or performative research.

Implications of Changes for the Researcher

The onus is now less on finding the “right” (or most familiar in a field) research approaches and following them meticulously, and much more on researchers making their own individual decisions as to which aspects of which methodologies, methods and theoretical explanations will best answer their research question. Ideally this should not be constrained by the state of the discipline they are part of; it should be equally as easy for a fine arts researcher to carry out a classical ethnography with a detailed theoretical interpretation derived from a grand theorist/s as it would be for a researcher in law to undertake a performative study with the minimum of conceptual insights and the maximum of visual and theoretical performances. Unfortunately, the reality is that trends within disciplines dictate publication access, thereby reinforcing the prevailing boundaries of knowledge.

However, the current diversity of choice has indeed shifted the field of qualitative research dramatically away from the position it was in several decades ago. The moves toward visual and performative displays may challenge certain disciplines but these approaches have now become well entrenched in others, and in qualitative research publishing. The creativity of the performative paradigm in daring to scale the siloed and well-protected boundaries of science in order to combine theoretical physics with the theories of social science, and to re-present data in a variety of newer ways from fiction to poetry to researcher performances, is exciting.

Given that theoretical as well as methodological and methods’ domains are now wide open to researchers to pick and choose from, two important aspects—justification and transparency of process—have become essential elements in the process of convincing the reader.

Justification incorporates the why of decision-making. Why was the research question chosen? Why was the particular paradigm, or paradigms, chosen best for the question? Why were the methodology and methods chosen most appropriate for both the paradigm/s and research question/s? And why were the concepts used the most appropriate and illuminating for the study?

Transparency of process not only requires that the researcher clarifies who they are in the field with relation to the research question and the participants chosen, but demands an assessment of what impact their background and personal and professional frames have had on research decisions at all stages from topic choice to theoretical analysis. Problems faced in the research process and how they were managed or overcome also requires exposition as does the chronology of decisions made and changed at all points of the research process.

Now to the issue of theory and the question of “where to?” This brief walk through the paradigmatic, methodological, and theoretical changes has demonstrated a significant move from the use of confined paradigms with limited methodological options to the availability of multiple paradigms, co-methodologies, and methods of many shades, for the researcher to select among Regarding theory use, there has been a clear move away from grand and middle range theories toward the application of individual concepts drawn from a variety of established and minor theoreticians and disciplines, which can be amalgamated into transitory explanations. The examples of theoretical interpretation presented in this article, in my view, very considerably extend, frame, and often shed new light on the themes that have been drawn out via analytical processes. Well-argued theory at any level is a great enhancer, lifting data to heights of illumination and comparison, but it could equally be argued that in the presence of critical researcher reflexivity, complex, layered, longitudinal, and well-justified design, meticulous analysis, and monitored audience response, it may no longer be essential.

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Chapter 13 Qualitative Analysis

Qualitative analysis is the analysis of qualitative data such as text data from interview transcripts. Unlike quantitative analysis, which is statistics driven and largely independent of the researcher, qualitative analysis is heavily dependent on the researcher’s analytic and integrative skills and personal knowledge of the social context where the data is collected. The emphasis in qualitative analysis is “sense making” or understanding a phenomenon, rather than predicting or explaining. A creative and investigative mindset is needed for qualitative analysis, based on an ethically enlightened and participant-in-context attitude, and a set of analytic strategies. This chapter provides a brief overview of some of these qualitative analysis strategies. Interested readers are referred to more authoritative and detailed references such as Miles and Huberman’s (1984) [17] seminal book on this topic.

Grounded Theory

How can you analyze a vast set qualitative data acquired through participant observation, in-depth interviews, focus groups, narratives of audio/video recordings, or secondary documents? One of these techniques for analyzing text data is grounded theory – an inductive technique of interpreting recorded data about a social phenomenon to build theories about that phenomenon. The technique was developed by Glaser and Strauss (1967) [18] in their method of constant comparative analysis of grounded theory research, and further refined by Strauss and Corbin (1990) [19] to further illustrate specific coding techniques – a process of classifying and categorizing text data segments into a set of codes (concepts), categories (constructs), and relationships. The interpretations are “grounded in” (or based on) observed empirical data, hence the name. To ensure that the theory is based solely on observed evidence, the grounded theory approach requires that researchers suspend any preexisting theoretical expectations or biases before data analysis, and let the data dictate the formulation of the theory.

Strauss and Corbin (1998) describe three coding techniques for analyzing text data: open, axial, and selective. Open coding is a process aimed at identifying concepts or key ideas that are hidden within textual data, which are potentially related to the phenomenon of interest. The researcher examines the raw textual data line by line to identify discrete events, incidents, ideas, actions, perceptions, and interactions of relevance that are coded as concepts (hence called in vivo codes ). Each concept is linked to specific portions of the text (coding unit) for later validation. Some concepts may be simple, clear, and unambiguous while others may be complex, ambiguous, and viewed differently by different participants. The coding unit may vary with the concepts being extracted. Simple concepts such as “organizational size” may include just a few words of text, while complex ones such as “organizational mission” may span several pages. Concepts can be named using the researcher’s own naming convention or standardized labels taken from the research literature. Once a basic set of concepts are identified, these concepts can then be used to code the remainder of the data, while simultaneously looking for new concepts and refining old concepts. While coding, it is important to identify the recognizable characteristics of each concept, such as its size, color, or level (e.g., high or low), so that similar concepts can be grouped together later . This coding technique is called “open” because the researcher is open to and actively seeking new concepts relevant to the phenomenon of interest.

Next, similar concepts are grouped into higher order categories . While concepts may be context-specific, categories tend to be broad and generalizable, and ultimately evolve into constructs in a grounded theory. Categories are needed to reduce the amount of concepts the researcher must work with and to build a “big picture” of the issues salient to understanding a social phenomenon. Categorization can be done is phases, by combining concepts into subcategories, and then subcategories into higher order categories. Constructs from the existing literature can be used to name these categories, particularly if the goal of the research is to extend current theories. However, caution must be taken while using existing constructs, as such constructs may bring with them commonly held beliefs and biases. For each category, its characteristics (or properties) and dimensions of each characteristic should be identified. The dimension represents a value of a characteristic along a continuum. For example, a “communication media” category may have a characteristic called “speed”, which can be dimensionalized as fast, medium, or slow . Such categorization helps differentiate between different kinds of communication media and enables researchers identify patterns in the data, such as which communication media is used for which types of tasks.

The second phase of grounded theory is axial coding , where the categories and subcategories are assembled into causal relationships or hypotheses that can tentatively explain the phenomenon of interest. Although distinct from open coding, axial coding can be performed simultaneously with open coding. The relationships between categories may be clearly evident in the data or may be more subtle and implicit. In the latter instance, researchers may use a coding scheme (often called a “coding paradigm”, but different from the paradigms discussed in Chapter 3) to understand which categories represent conditions (the circumstances in which the phenomenon is embedded), actions/interactions (the responses of individuals to events under these conditions), and consequences (the outcomes of actions/ interactions). As conditions, actions/interactions, and consequences are identified, theoretical propositions start to emerge, and researchers can start explaining why a phenomenon occurs, under what conditions, and with what consequences.

The third and final phase of grounded theory is selective coding , which involves identifying a central category or a core variable and systematically and logically relating this central category to other categories. The central category can evolve from existing categories or can be a higher order category that subsumes previously coded categories. New data is selectively sampled to validate the central category and its relationships to other categories (i.e., the tentative theory). Selective coding limits the range of analysis, and makes it move fast. At the same time, the coder must watch out for other categories that may emerge from the new data that may be related to the phenomenon of interest (open coding), which may lead to further refinement of the initial theory. Hence, open, axial, and selective coding may proceed simultaneously. Coding of new data and theory refinement continues until theoretical saturation is reached, i.e., when additional data does not yield any marginal change in the core categories or the relationships.

The “constant comparison” process implies continuous rearrangement, aggregation, and refinement of categories, relationships, and interpretations based on increasing depth of understanding, and an iterative interplay of four stages of activities: (1) comparing incidents/texts assigned to each category (to validate the category), (2) integrating categories and their properties, (3) delimiting the theory (focusing on the core concepts and ignoring less relevant concepts), and (4) writing theory (using techniques like memoing, storylining, and diagramming that are discussed in the next chapter). Having a central category does not necessarily mean that all other categories can be integrated nicely around it. In order to identify key categories that are conditions, action/interactions, and consequences of the core category, Strauss and Corbin (1990) recommend several integration techniques, such as storylining, memoing, or concept mapping. In storylining , categories and relationships are used to explicate and/or refine a story of the observed phenomenon. Memos are theorized write-ups of ideas about substantive concepts and their theoretically coded relationships as they evolve during ground theory analysis, and are important tools to keep track of and refine ideas that develop during the analysis. Memoing is the process of using these memos to discover patterns and relationships between categories using two-by-two tables, diagrams, or figures, or other illustrative displays. Concept mapping is a graphical representation of concepts and relationships between those concepts (e.g., using boxes and arrows). The major concepts are typically laid out on one or more sheets of paper, blackboards, or using graphical software programs, linked to each other using arrows, and readjusted to best fit the observed data.

After a grounded theory is generated, it must be refined for internal consistency and logic. Researchers must ensure that the central construct has the stated characteristics and dimensions, and if not, the data analysis may be repeated. Researcher must then ensure that the characteristics and dimensions of all categories show variation. For example, if behavior frequency is one such category, then the data must provide evidence of both frequent performers and infrequent performers of the focal behavior. Finally, the theory must be validated by comparing it with raw data. If the theory contradicts with observed evidence, the coding process may be repeated to reconcile such contradictions or unexplained variations.

Content Analysis

Content analysis is the systematic analysis of the content of a text (e.g., who says what, to whom, why, and to what extent and with what effect) in a quantitative or qualitative manner. Content analysis typically conducted as follows. First, when there are many texts to analyze (e.g., newspaper stories, financial reports, blog postings, online reviews, etc.), the researcher begins by sampling a selected set of texts from the population of texts for analysis. This process is not random, but instead, texts that have more pertinent content should be chosen selectively. Second, the researcher identifies and applies rules to divide each text into segments or “chunks” that can be treated as separate units of analysis. This process is called unitizing . For example, a ssumptions, effects, enablers, and barriers in texts may constitute such units. Third, the researcher constructs and applies one or more concepts to each unitized text segment in a process called coding . For coding purposes, a coding scheme is used based on the themes the researcher is searching for or uncovers as she classifies the text. Finally, the coded data is analyzed, often both quantitatively and qualitatively, to determine which themes occur most frequently, in what contexts, and how they are related to each other.

A simple type of content analysis is sentiment analysis – a technique used to capture people’s opinion or attitude toward an object, person, or phenomenon. Reading online messages about a political candidate posted on an online forum and classifying each message as positive, negative, or neutral is an example of such an analysis. In this case, each message represents one unit of analysis. This analysis will help identify whether the sample as a whole is positively or negatively disposed or neutral towards that candidate. Examining the content of online reviews in a similar manner is another example. Though this analysis can be done manually, for very large data sets (millions of text records), natural language processing and text analytics based software programs are available to automate the coding process, and maintain a record of how people sentiments fluctuate with time.

A frequent criticism of content analysis is that it lacks a set of systematic procedures that would allow the analysis to be replicated by other researchers. Schilling (2006) [20] addressed this criticism by organizing different content analytic procedures into a spiral model. This model consists of five levels or phases in interpreting text: (1) convert recorded tapes into raw text data or transcripts for content analysis, (2) convert raw data into condensed protocols, (3) convert condensed protocols into a preliminary category system, (4) use the preliminary category system to generate coded protocols, and (5) analyze coded protocols to generate interpretations about the phenomenon of interest.

Content analysis has several limitations. First, the coding process is restricted to the information available in text form. For instance, if a researcher is interested in studying people’s views on capital punishment, but no such archive of text documents is available, then the analysis cannot be done. Second, sampling must be done carefully to avoid sampling bias. For instance, if your population is the published research literature on a given topic, then you have systematically omitted unpublished research or the most recent work that is yet to be published.

Hermeneutic Analysis

Hermeneutic analysis is a special type of content analysis where the researcher tries to “interpret” the subjective meaning of a given text within its socio-historic context. Unlike grounded theory or content analysis, which ignores the context and meaning of text documents during the coding process, hermeneutic analysis is a truly interpretive technique for analyzing qualitative data. This method assumes that written texts narrate an author’s experience within a socio-historic context, and should be interpreted as such within that context. Therefore, the researcher continually iterates between singular interpretation of the text (the part) and a holistic understanding of the context (the whole) to develop a fuller understanding of the phenomenon in its situated context, which German philosopher Martin Heidegger called the hermeneutic circle. The word hermeneutic (singular) refers to one particular method or strand of interpretation.

More generally, hermeneutics is the study of interpretation and the theory and practice of interpretation. Derived from religious studies and linguistics, traditional hermeneutics, such as biblical hermeneutics , refers to the interpretation of written texts, especially in the areas of literature, religion and law (such as the Bible). In the 20th century, Heidegger suggested that a more direct, non-mediated, and authentic way of understanding social reality is to experience it, rather than simply observe it, and proposed philosophical hermeneutics , where the focus shifted from interpretation to existential understanding. Heidegger argued that texts are the means by which readers can not only read about an author’s experience, but also relive the author’s experiences. Contemporary or modern hermeneutics, developed by Heidegger’s students such as Hans-Georg Gadamer, further examined the limits of written texts for communicating social experiences, and went on to propose a framework of the interpretive process, encompassing all forms of communication, including written, verbal, and non-verbal, and exploring issues that restrict the communicative ability of written texts, such as presuppositions, language structures (e.g., grammar, syntax, etc.), and semiotics (the study of written signs such as symbolism, metaphor, analogy, and sarcasm). The term hermeneutics is sometimes used interchangeably and inaccurately with exegesis , which refers to the interpretation or critical explanation of written text only and especially religious texts.

Conclusions

Finally, standard software programs, such as ATLAS.ti.5, NVivo, and QDA Miner, can be used to automate coding processes in qualitative research methods. These programs can quickly and efficiently organize, search, sort, and process large volumes of text data using user-defined rules. To guide such automated analysis, a coding schema should be created, specifying the keywords or codes to search for in the text, based on an initial manual examination of sample text data. The schema can be organized in a hierarchical manner to organize codes into higher-order codes or constructs. The coding schema should be validated using a different sample of texts for accuracy and adequacy. However, if the coding schema is biased or incorrect, the resulting analysis of the entire population of text may be flawed and non-interpretable. However, software programs cannot decipher the meaning behind the certain words or phrases or the context within which these words or phrases are used (such as those in sarcasms or metaphors), which may lead to significant misinterpretation in large scale qualitative analysis.

[17] Miles M. B., Huberman A. M. (1984). Qualitative Data Analysis: A Sourcebook of New Methods . Newbury Park, CA: Sage Publications.

[18] Glaser, B. and Strauss, A. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research , Chicago: Aldine.

[19] Strauss, A. and Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques , Beverly Hills, CA: Sage Publications.

[20] Schilling, J. (2006). “On the Pragmatics of Qualitative Assessment: Designing the Process for Content Analysis,” European Journal of Psychological Assessment (22:1), 28-37.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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About Qualitative Data

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

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

(From the Data Glossary , National Center for Data Services, National Library of Medicine)

Methods Texts

Below are some methods texts recommended by qualitative workshop leaders from the UC Berkeley Library and the D-Lab: 

Workshops and Training

• Managing qualitative data 101 Tips on managing qualitative materials from your qualitative research librarian.
• D-Lab workshops Free online workshops on quant and qualitative skills, including coding and using qualitative analysis software.
• Institute for the Study of Societal Issues (ISSI) Training Ethnographic methods workshop from a campus institute.
• Qualitative Methods classes Filter to upcoming semesters and look for qualitative methods classes; the Graduate School of Education and School of Public Health offer extensive methods training.

Qualitative Data Analysis Software

Unfortunately, Berkeley does not yet have a sitewide license for any qualitative analysis software.

If you are a student, you can find affordable student licenses with a web search.

If you are a faculty member, instructor, lecturer, or visiting scholar without grant funding, unfortunately software is quite expensive.

You can find reviews of many qualitative software packages at this University of Surrey link:

• Choosing an Appropriate CAQDAS package .

You can also check out the websites of several major options below: 

• Taguette Taguette has fewer features than other qualitative analysis software, but is free and open-source.
• Atlas.ti Atlas.ti is a major qualitative analysis software, and has affordable licenses for students.
• MaxQDA MaxQDA is a major qualitative analysis software, with affordable student licenses. The D-Lab often teaches workshops on this software.
• NVIVO NVIVO is an established QDA software, with affordable student licenses.
• Dedoose Dedoose supports qualitaive and mixed methods research, using an online interface. Students pay $11 per month.

Resources for Qualitative Data Management

• Managing and Sharing Qualitative Data 101 This page from Berkeley's research data management website offers several things to consider.
• Tutorials on Ethnographic Data Management This curricula includes eight presentations and accompanying exercises for you to think through your qualitative data project--or coach others to do the same.
• Support Your Data: Evaluation Rubric Download the evaluation rubric on this page to assess where you are with qualitative data management, and consider areas to explore next.
• The Qualitative Data Repository (QDR) QDR is one of the top US-based repositories focused on the challenges of managing, storing, and sharing qualitative research materials.
• Research Data @ Berkeley Email Research Data for a consultation about how to set up your qualitative data management plan; they can help you locate other resources on campus.

Mixed Methods Research

Interpretations related to mixed (sometimes called merged) methods vary; be wary of jargon!  Gery Ryan, of the Kaiser Permanente School of Medicine, gives these definitions, while arguing that we should be thinking of the purposes of the research rather than the methodological labels:

Mixed methods research : “Combines elements of qualitative and quantitative research approaches (e. g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.”

Multimethod research : “Either solely combine multiple qualitative approaches or solely combine multiple quantitative approaches.”

Data triangulation : “Uses multiple sources of data or multiple approaches to analyzing data to enhance the credibility of a research study.”

(From " Mixed Methods Research Designs and Data Triangulation " by Gery Ryan, Kaiser Permanente School of Medicine)

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• Next: Data Science Methods (Machine Learning, AI, Big Data) >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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86 Comments

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Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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do you have any material on Data collection

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Research Methods: Qualitative Research and Quantitative Research

Design of Experiments > Research Methods

Quantitative Research vs. Qualitative.

Quantitative research is statistical: it has numbers attached to it, like averages, percentages or quotas. Qualitative research uses non-statistical methods. For example, you might perform a study and find that 50% of a district’s students dislike their teachers. The quantity (50%) makes it quantitative research. A follow up qualitative study could interview a small percentage of those students to find out why . The answers are free-form and don’t have numbers associated with them, so that makes them qualitative.

Contents (Click to skip to that section):

• Characteristics of Qualitative Research Methods.

Structured and Semistructured Interviews

• Types of Qualitative Research.

Advantages and Disadvantages of Qualitative Research Methods.

• Multi-Qualitative Research Methods Approach.

Quantitative Research Methods.

Basic research methods., what is qualitative research.

Qualitative research(QR) is way to gain a deeper understanding of an event, organization or culture. Depending on what type of phenomenon you are studying, QR can give you a broad understanding of events, data about human groups, and broad patterns behind events and people. While traditional lab-based research looks for a specific “something” in the testing environment, qualitative research allows the meaning, themes, or data to emerge from the study.

Qualitative research uses non-statistical methods to gain understanding about a population. In other words, you’re not dealing with the numbers you’d find in quantitative research. For example, let’s say your research project was to answer the question “Why do people buy fast food?”. Instead of a survey (which can usually be analyzed with math), you might use in-depth interviews to gain a deeper understanding of people’s motives. Another major difference between qualitative and quantitative research is that QR is usually performed in a natural setting (As opposed to a lab).

Characteristics of Qualitative Research.

All of the different qualitative research methods have several characteristics:

• Findings are judged by whether they make sense and are consistent with the collected data.
• Results are validated externally by how well they might be applicable to other situations. This is tough to do; rich, detailed descriptions can help to bolster external validity .
• Data is usually collected from small, specific and non-random sample s.

Although qualitative research doesn’t have the same structure as a formal lab-testing environment, there are certain requirements you must meet in order for your qualitative study to be called “research.” Your study must:

• Have a formulated research purpose . For example “Examine the lifestyles of Chinese immigrants.”
• Be related to existing theories, published or unpublished. You can’t just make up an idea that has no basic in current thinking. For example, a study to see how immigrants cope in the workplace would build on previous, similar studies. However, there would be no previous theories for something out-of-left field like “Why do Italian immigrants prefer Pepsi?”
• Be well-planned. You can’t go into the jungle with no plan and no idea of how you’re going accomplish your goals.
• Be recorded carefully with notes and other media like film or voice recordings. If you don’t take careful notes, you could miss something of vital importance.

Other rules you must follow include selecting the people or events you want to observe, having a plan on how you’re going to get into the “world” you want to observe, and deciding ahead of time what types of data you’re going to gather.

Back to Top

A semistructured interview is a way of research which allows room for new information and insights to be incorporated as the interview is run.

While structured interviews are based on a well-defined set of questions that are settled beforehand, semistructured interviews work from only a general framework of themes. A number of questions may be determined beforehand, especially initial questions, but freedom is left for deviation—within limits. The interviewer is expected to formulate most questions on the fly, based on the interviewees responses, and has the freedom to follow relevant tangents and ideas as they come up throughout the interview.

A semi-structured interview is more structured than an open interview, in which the interviewee is expected to chat about whichever topic interests him, but it still gives a fair degree of freedom and allows the interviewee to take the initiative to a degree.

Advantages and Disadvantages of a Semistructured Interview

A semi structured interview format is often chosen because the open format mitigates bias that might be imposed by the leading questions which often form a part of structured interviews. It also allows an interviewer to incorporate new information and follow new ideas as they come up in the interview, without being bound by a preconceived set of ideas.

The main negative of using a semi structured interview format is that it requires some skill on the part of the interviewer, who must be able to establish rapport with the interviewee and allow enough room to explore related ideas while still keeping focus. Data from semi structured interviews is also typically harder to organize and analyze than data from a structured interview might be.

Planning and Conducting a Semi Structured Interview

While you don’t need to write down a complete set of questions for a semistructured interview, you will want to plan extensively:

• Prepare a thorough list of topics and write down some tentative questions.
• Think about different ways of approaching the ideas you want to discuss.
• Make sure the interview site is comfortable and conducive to discussion, as the success of your interview depends on your interviewee feeling free to talk.
• Begin by introducing your topic and research, then ask a few general background questions.

Go on to your topic, asking questions that instigate discussion (as opposed to yes/no replies). Allow the interviewee freedom to expand on side issues that particularly interest him, but be prepared to bring the topic back to the main topic as needed. At the end of the interview, give the interviewee a chance to share any additional points he or she feels is important.

Zorn, Ted. Designing and Conducting SemiStructured Interviews for Research. Retrieved from http://home.utah.edu/~u0326119/Comm4170-01/resources/Interviewguidelines.pdf on June 23, 2018. Jamshed, Shazia. Qualitative research method-interviewing and observation. Journal of Basic and Clinical Pharmacy. September 2014-November 2014; 5(4): 87–88. doi: 10.4103/0976-0105.141942. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194943/ on June 23, 2018. Oxfam. Conducting Semi-structured Interviews. Retrieved from https://itp.nyu.edu/classes/fungus/interview_technique/conductingInterviews.pdf on June 24, 2018

Types of Qualitative Research Methods.

Anthropological.

Autoethnography.

In this research method, you use your own experiences to address a cultural, political, or social issue. It is considered by many to be a non-traditional ethnographic method. This type of research can involve several people. For example, a group of immigrant women researchers conducted a study on how they navigated the US academy as immigrant women faculty (Ngunjiri et. al 2010).

Critical Social Research.

Critical social research studies specific oppressive social structures (Harvey, 1990). This type of research attempts to expose problems, evaluate the problems and find their root causes. For example, critical social research could attempt to uncover cases of juvenile crime, racism, or suicide. The main difference between this type of research and other qualitative types is that there is always “a problem” that needs “fixing” going into the study . The research question revolves around an existing, known, problem. Traditional research uncovers problems or issues with interviews, data collection and other QR methods.

Ethical Inquiry.

Ethical inquiry is a research method used in philosophy to answer ethical questions such as Is it ethical to eat animals ? or The Ethics of HIV Criminalization.

Ethnographic Research

Ethnography is the study of people in their own environment through methods like participant observation and face-to-face interviewing. According to ethnographer David Fetterman , this method gives a voice to people in their own local context, giving a complete description of events as they happen.

[Ethnography] is about telling a credible, rigorous, and authentic story.

There is a small, but important difference between ethnographic research and anthropological research:

• Anthropological research involves all of the cultures on the planet,
• Classic ethnographic research provides a detailed description of an complete culture outside of the researcher’s country of origin (Ingold, 2008).

Traditionally, ethnographers spend at least one year inside the culture they are studying, relying heavily on participant observation as the key data collection method. Other methods used include field notes, focus groups, interviews, and surveys.

Field Research.

Field research is research outside of a lab, in a natural setting. This type of research usually involves first hand note-taking. It may also include video footage, interviews with experts in the area being studied, conducting surveys or attending public discussion forums.

Grounded Theory Research.

Grounded theory is often categorized as a qualitative research method, but technically it can be applied to either quantitative research or qualitative research; it’s a general research method involving a set of rigorous procedures. The result is (hopefully) a set of conceptual data categories. The “Theory” in grounded theory refers to the theory of what you are studying. For example, you might have a theory about the eating habits of the Nez Perce tribe. The “Grounded” part refers to the fact that your theory needs to be grounded in research . That’s the simple definition. In reality, it’s a type of QR that’s poorly understood, with many researchers claiming they used it, when in fact they did not. It’s actually a fairly complicated process that builds upon itself. You start by generating questions to guide your research. These questions identify core concepts, which lead you to identifying links between your questions and your data. This part of the process can take months.

Naturalistic.

Naturalistic research is research that doesn’t manipulate anything in the environment . In other words, it’s the opposite of a lab environment where variables are manipulated on purpose. Care should be taken with naturalistic research, as even your presence can alter the environment–taking away the “naturalistic” component. Bias can easily creep in to these types of studies; two people can have different viewpoints of the same thing. It’s a common safeguard to have two or more researchers observing the same thing so that any differences in viewpoint can be addressed.

Participant Observer Research.

Phenomenology.

Phenomenology studies someone’s perception of an event. The focus of this type of study is what people’s experiences are for a particular event and how they interpret their experiences. For example: a study of Hurricane Katrina survivors perceptions, understandings, and perspectives of the hurricane.

Back to Top.

Qualitative research is not part of statistical analysis. That’s because the results can’t be tested to see if they are statistically significant (i.e. to see if the results could have occurred by chance). As a result, findings can’t be extended to a wider population. That doesn’t mean this type of research is useless: in many studies, getting hard numbers is inappropriate or just impossible.

Advantages : If qualitative research can’t be used to estimate statistics for a population , why use it at all? One reason is that while statistics concentrates on specific, narrow areas (for example, population means , medians or standard deviations ), qualitative analysis paints a wider, complete picture. In addition, phenomena that’s rare receives the same level of attention as more common phenomena. Other advantages include:

• It’s useful for finding out more about complex situations.
• Allows the use of an “insider viewpoint.”
• Data is based on the participant’s views of the world, rather than a world created by a researcher.
• Can be used to figure out how people interpret constructs like IQ or fear, which can be hard to quantify.
• The study focus can be shifted in the middle of research, if necessary. In a traditional lab setting, this would usually null-and-void the experiment.
• An important case can be used to vividly paint a picture in a report.

Disadvantages: One of the main disadvantages to qualitative research is that your data usually can’t be generalized outside of your research. For example, if you find that an Asian street gang has a certain hierarchy, then that hierarchy likely exists only within Asian street gangs, and perhaps only in the particular gang you studied.

• Predictions of future events are usually impossible to quantify (i.e. you wouldn’t be able to say “there is a 95% chance of this event happening in the future.”).
• Qualitative research, in general, has lower credibility than quantitative research. This may make it more difficult to get your results published.
• Data collection takes a lot longer than in a traditional lab setting.
• Your own personal biases and other idiosyncrasies are more likely to affect the research.

Multi-Qualitative Research Method Approaches

Both qualitative and quantitative research methods have their limitations. There is a recent trend towards a multi-method research approach which uses both types to:

• Quantify phenomena and make sure it’s statistically sound.
• Paint a broader picture of the phenomena.

What is Quantitative Research?

• What percentage of high school teachers belong to minority groups?
• How many females in college study mathematics compared to males?
• Has the high school graduation rate in our district increased over time?

However, data doesn’t always naturally happen in a numerical way. You may want to answer questions like:

• What do high school students think of their teachers?
• What is the general public opinion of health care reform?
• What do customers at a particular business think of customer service?

These questions aren’t immediately quantifiable, but you can turn them into quantifiable questions by assigning numbers to them. For example, you could make a survey with the following question and responses: “I think that customer service at this business is excellent.”

• Strongly Agree.
• No opinion.
• Strongly disagree.

Elements of Quantitative Research Methods.

The crucial elements of quantitative research design are:

• Choice of data collection instrument. Typical methods for data collection are surveys or questionnaires with closed-ended questions, using data from another source (for example, a government database) or an experiment with a control group and an experimental group .
• Choice of analysis tool (i.e. statistics). For example, you might choose to report your results using confidence intervals and test statistics from t tests or f tests with significance levels ( alpha levels ) and p values .

• Archival research. Researching through archives: rare books, historical records and other historical data like school yearbooks.
• Content analysis . Content analysis looks at whether certain words or phrases are present in texts. Inferences can then be made about the writer, the audience and the culture surrounding those texts.
• Case study. Case studies are usually performed on an individual or small group of individuals. A detailed history is taken of some aspect of the participant’s life. For example, you might make a case study of a group of 12 alcoholics on their drinking habits, personal history and home life.
• Computer simulation. Computer modeling is one of the research methods gradually becoming more popular especially, where ethical constraints prevent actual experiments or observation. For example, instead of watching the effects of a poisonous gas on mice, a computer simulation can model the effect without the need for live animals .
• Experiment . An experiment is where you deliberately manipulate one variable (the independent variable ) to see what the outcome is on another variable (the dependent variable ). Experiments are typically performed in a closed setting, like a laboratory. Field experiments are where the experiment takes place outside of the controlled setting, in the real world.
• Interview . In an interview, you meet face to face with participants. Taping an interview is usually preferable, as note-taking can be distracting. Interview questions can be closed–where participants are given a choice of answers–or open questions, which allow for thoughtful, in-depth responses. An interview can also be a mixture of both.
• Meta-analysis. A meta-analysis combines the findings from existing research into one, comprehensive thesis. A meta-analysis can uncover trends or themes that weren’t apparent in individual pieces of research.
• Observational Study. A type of study where the researcher observes participants without any kind of interference. Participants are placed into two groups with one control group and one experimental group (i.e. smokers and non-smokers).
• Unobtrusively observing wild chimpanzees
• Sitting in a coffee shop and observing interactions between patrons
• Survey . Surveys usually involve a representative sample of the population, using a technique like random sampling. A questionnaire is given to each member of the sample and used to infer characteristics of the whole population . Surveys are easy in theory but can be difficult to put into practice, mainly because of a typically low response rate.

Asad, T. (1994). Ethnographic Representation, Statistics and Modern Power. Social Research Vol. 61, No. 1. Retrieved January 6, 2021 from: https://www.jstor.org/stable/40971022?seq=1 Atkinson, P. et al. (Eds.). Handbook of Ethnography 1st Edition . SAGE Publications. Beyer, W. H. CRC Standard Mathematical Tables, 31st ed. Boca Raton, FL: CRC Press, pp. 536 and 571, 2002. Dodge, Y. (2008). The Concise Encyclopedia of Statistics . Springer. Fetterman, D. (2009). Chapter 17: Ethnography. In The SAGE Handbook of Applied Social Research Methods. Ingold, T. (2008). Anthropology is not ethnography. Proceedings of the British Academy, 154, 69-92. Kotz, S.; et al., eds. (2006), Encyclopedia of Statistical Sciences , Wiley. Vogt, W.P. (2005). Dictionary of Statistics & Methodology: A Nontechnical Guide for the Social Sciences . SAGE.

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
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• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
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• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

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Constructivist Grounded Theory

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Reliability prediction for new prefabricated track structures based on the fuzzy failure modes, effects, and criticality analysis method.

1. Introduction

2. the fuzzy fmeca method, 2.1. construct the factor set, 2.2. constructing the evaluation set, 2.3. constructing the fuzzy evaluation matrix, 2.4. constructing the weight vector, 2.4.1. calculate the entropy value, 2.4.2. construct the weight vector, 2.5. determine the criticality, 3. fuzzy fmeca for new prefabricated track structures, 3.1. composition of track structures, 3.2. fmeca of track structures, 3.3. fuzzy fmeca of track structures, 3.3.1. construction of factor set, 3.3.2. construction of evaluation set, 3.4. constructing the weight vector, 3.5. determining criticality, 4. reliability prediction for new prefabricated track structures, 4.1. reliability prediction model based on a similar product method, 4.2. reliability prediction, 5. discussion, 6. conclusions.

• The overall criticality value of the two track structures was calculated using the fuzzy FMECA method. The overall criticality value of the CRTS II slab track structure was 34.7464, while the overall criticality value of the modular assembled track structure with built-in position retention was 33.5803, which indicates that the latter has lower criticality.
• A reliability prediction model based on the similar product method was established, and the reliability of the modular assembled track structure with built-in position retention was quantitatively estimated. According to the reliability prediction model, the reliability of the modular assembled track structure with built-in position retention is 0.9994, which indicates a high level of reliability.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Huang, C.; Wu, J.; Shan, Z.; Wang, Q.; Yu, Z. Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method. Appl. Sci. 2024 , 14 , 5338. https://doi.org/10.3390/app14125338

Huang C, Wu J, Shan Z, Wang Q, Yu Z. Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method. Applied Sciences . 2024; 14(12):5338. https://doi.org/10.3390/app14125338

Huang, Chao, Jun Wu, Zhi Shan, Qing’e Wang, and Zhiwu Yu. 2024. "Reliability Prediction for New Prefabricated Track Structures Based on the Fuzzy Failure Modes, Effects, and Criticality Analysis Method" Applied Sciences 14, no. 12: 5338. https://doi.org/10.3390/app14125338

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