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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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Importance of Quantitative Research Across Fields

First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).

Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.

In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.

Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.

First, on the importance of quantitative research in psychology .

We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.

It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.

Second, on the importance of quantitative research in economics .

In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.

Third, on the importance of quantitative research in education .

Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.

In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.

Fourth, on the importance of quantitative research in Environmental Science and Sustainability.

Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.

But how does quantitative research provide the context for environmental science and sustainability?

Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.

And fifth, on the importance of quantitative research in business .

As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a  market research  assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.

Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.

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The qualitative orientation in medical education research

Qualitative research is very important in educational research as it addresses the “how” and “why” research questions and enables deeper understanding of experiences, phenomena and context. Qualitative research allows you to ask questions that cannot be easily put into numbers to understand human experience. Getting at the everyday realities of some social phenomenon and studying important questions as they are really practiced helps extend knowledge and understanding. To do so, you need to understand the philosophical stance of qualitative research and work from this to develop the research question, study design, data collection methods and data analysis. In this article, I provide an overview of the assumptions underlying qualitative research and the role of the researcher in the qualitative process. I then go on to discuss the type of research objectives which are common in qualitative research, then introduce the main qualitative designs, data collection tools, and finally the basics of qualitative analysis. I introduce the criteria by which you can judge the quality of qualitative research. Many classic references are cited in this article, and I urge you to seek out some of these further reading to inform your qualitative research program.

Introduction

When we speak of “quantitative” or “qualitative” methodologies, we are in the final analysis speaking about an interrelated set of assumptions about the social world which are philosophical, ideological, and epistemological. They encompass more than just data collection methodologies [ 1 ].

It is easy to assume that the differences between quantitative and qualitative research are solely about how data is collected—the randomized controlled trial versus ethnographic fieldwork, the cohort study versus the semi-structured interview. However, quantitative and qualitative approaches make different assumptions about the world [ 2 ], about how science should be conducted, and about what constitutes legitimate problems, solutions and criteria of “proof” [ 3 ].

Why is it important to understand differences in assumptions, or philosophies, of research? Why not just go ahead and do a survey or carry out some interviews? First, the assumptions behind the research tools you choose provide guidance for conducting your research. They indicate whether you should be an objective observer or whether you have a contributory role in the research process. They guide whether or not you must slavishly ask each person in a study the same questions or whether your questions can evolve as the study progresses. Second, you may wish to submit your work as a dissertation or as a research paper to be considered for publication in a journal. If so, the chances are that examiners, editors, and reviewers might have knowledge of different research philosophies from yours and may be unwilling to accept the legitimacy of your approach unless you can make its assumptions clear. Third, each research paradigm has its own norms and standards, its accepted ways of doing things. You need to “do things right”. Finally, understanding the theoretical assumptions of the research approach helps you recognize what the data collection and analysis methods you are working with do well and what they do less well, and lets you design your research to take full advantage of their strengths and compensate for their weaknesses.

In this short article, I will introduce the assumptions of qualitative research and their implications for research questions, study design, methods and tools, and analysis and interpretation. Readers who wish a comparison between qualitative and quantitative approaches may find Cleland [ 4 ] useful.

Ontology and epistemology

We start with a consideration of the ontology (assumptions about the nature of reality) and epistemology (assumptions about the nature of knowledge) of qualitative research.

Qualitative research approaches are used to understand everyday human experience in all its complexity and in all its natural settings [ 5 ]. To do this, qualitative research conforms to notions that reality is socially constructed and that inquiry is unavoidably value-laden [ 6 ]. The first of these, reality is socially constructed, means reality cannot be measured directly—it exists as perceived by people and by the observer. In other words, reality is relative and multiple, perceived through socially constructed and subjective interpretations [ 7 ]. For example, what I see as an exciting event may be seen as a threat by other people. What is considered a cultural ritual in my country may be thought of as quite bizarre elsewhere. Qualitative research is concerned with how the social world is interpreted, understood, experienced, or constructed. Mann and MacLeod [ 8 ] provide a very good overview of social constructivism which is a excellent starting point for understanding this.

The idea of people seeing things in diverse ways also holds true in research process, hence inquiry being valued-laden. Different people have different views of the same thing depending on their upbringing and other experiences, their training, and professional background. Someone who has been trained as a social scientist may “see” things differently from someone who has been medically trained. A woman may see things differently to a man. A more experienced researcher will see things differently from a novice. A qualitative researcher will have very different views of the nature of “evidence” than a quantitative researcher. All these viewpoints are valid. Moreover, different researchers can study the same topic and try to find solutions to the same challenges using different study designs—and hence come up with different interpretations and different recommendations. For example, if your position is that learning is about individual, cognitive, and acquisitive processes, then you are likely to research the use of simulation training in surgery in terms of the effectiveness and efficacy of training related to mastery of technical skills [ 9 , 10 ]. However, if your stance is that learning is inherently a social activity, one which involves interactions between people or groups of people, then you will look to see how the relationships between faculty members, participants and activities during a simulation, and the wider social and cultural context, influence learning [ 11 , 12 ].

Whether researchers are explicit about it or not, ontological and epistemological assumptions will underpin how they study aspects of teaching and learning. Differences in these assumptions shape not only study design, but also what emerges as data, how this data can be analysed and even the conclusions that can be drawn and recommendations that can be made from the study. This is referred to as worldview, defined by Creswell [ 13 ] as “a general orientation about the world and the nature of research that a researcher holds.” McMillan [ 14 ] gives a very good explanation of the importance of this phenomenon in relation to medical education research. There is increasing expectation that researchers make their worldview explicit in research papers.

The research objective

Given the underlying premise that reality is socially constructed, qualitative research focuses on answering “how” and “why” questions, of understanding a phenomena or a context. For example, “Our study aimed to answer the research question: why do assessors fail to report underperformance in medical students? [ 15 ]”, “The aim of this work was to investigate how widening participation policy is translated and interpreted for implementation at the level of the individual medical school [ 4 ].”

Common verbs in qualitative research questions are identify, explore, describe, understand, and explain. If your research question includes words like test or measure or compare in your objectives, these are more appropriate for quantitative methods, as they are better suited to these types of aims. Bezuidenhout and van Schalkwyk [ 16 ] provide a good guide to developing and refining your research question. Lingard [ 17 ]’s notion of joining the conversation and the problem-gap-hook heuristic are also very useful in terms of thinking about your question and setting it out in the introduction to a paper in such a way as to interest journal editors and readers.

Do not think formulating a research question is easy. Maxwell [ 18 ] gives a good overview of some of the potential issues including being too general, making assumptions about the nature of the issue/problem and using questions which focus the study on difference rather than process. Developing relevant, focused, answerable research questions takes time and generating good questions requires that you pay attention not just to the questions themselves but to their connections with all the other components of the study (the conceptual lens/theory, the methods) [ 18 ].

Theory can be applied to qualitative studies at different times during the research process, from the selection of the research phenomenon to the write-up of the results. The application of theory at different points can be described as follows [ 19 , 20 , 21 ]: (1) Theory frames the study questions, develops the philosophical underpinnings of the study, and makes assumptions to justify or rationalize the methodological approach. (2) Qualitative investigations relate the target phenomenon to the theory. (3) Theory provides a comparative context or framework for data analysis and interpretation. (4) Theory provides triangulation of study findings.

Schwartz-Barcott et al. [ 20 ] characterized those processes as theoretical selectivity (the linking of selected concepts with existing theories), theoretical integration (the incorporation and testing of selected concepts within a particular theoretical perspective), and theory creation (the generation of relational statements and the development of a new theory). Thus, theory can be the outcome of the research project as well as the starting point [ 22 ].

However, the emerging qualitative researcher may wish a little more direction on how to use theory in practice. I direct you to two papers: Reeves et al. [ 23 ] and Bordage [ 24 ]. These authors clearly explain the utility of theory, or conceptual frameworks, in qualitative research, how theory can give researchers different “lenses” through which to look at complicated problems and social issues, focusing their attention on different aspects of the data and providing a framework within which to conduct their analysis. Bordage [ 24 ] states that “conceptual frameworks represent ways of thinking about a problem or a study, or ways of representing how complex things work the way they do. Different frameworks will emphasise different variables and outcomes.” He presents an example in his paper and illustrates how different lens highlight or emphasise different aspects of the data. Other authors suggest that two theories are potentially better than one in exploring complex social issues [ 25 ]. There is an example of this in one of my papers, where we used the theories of Bourdieu [ 26 ] and Engestrom [ 27 , 28 ] nested within an overarching framework of complexity theory [ 29 ] to help us understand learning at a surgical bootcamp. However, I suggest that for focused studies and emerging educational researchers, one theoretical framework or lens is probably sufficient.

So how to identify an appropriate theory, and when to use it? It is crucially important to read widely, to explore lots of theories, from disciplines such as (but not only) education, psychology, sociology, and economics, to see what theory is available and what may be suitable for your study. Carefully consider any theory, check its assumptions [ 30 ] are congruent with your approach, question, and context before final selection [ 31 ] before deciding which theory to use. The time you spend exploring theory will be time well spent in terms not just of interpreting a specific data set but also to broadening your knowledge. The second question, when to use it, depends on the nature of the study, but generally the use of theory in qualitative research tends to be inductive; that is, building explanations from the ground up, based on what is discovered. This typically means that theory is brought in at the analysis stage, as a lens to interpret data.

In the qualitative approach, the activities of collecting and analyzing data, developing and modifying theory, and elaborating or refocusing the research questions, are usually going on more or less simultaneously, each influencing all of the others for a useful model of qualitative research design [ 18 ]. The researcher may need to reconsider or modify any design decision during the study in response to new developments. In this way, qualitative research design is less linear than quantitative research, which is much more step-wise and fixed.

This is not the same as no structure or plan. Most qualitative projects are pre-structured at least in terms of the equivalent of a research protocol, setting out what you are doing (aims and objectives), why (why is this important), and how (theoretical underpinning, design, methods, and analysis). I have provided a brief overview of common approaches to qualitative research design below and direct you to the numerous excellent textbooks which go into this in more detail [ 32 , 33 , 34 , 35 ].

There are five basic categories of qualitative research design: ethnography, narrative, phenomenological, grounded theory, and case study [ 13 , 32 ].

2. Ethnography

In ethnography, you immerse yourself in the target participants’ environment to understand the goals, cultures, challenges, motivations, and themes that emerge. Ethnography has its roots in cultural anthropology where researchers immerse themselves within a culture, often for years. Through multiple data collection approaches—observations, interviews and documentary data, ethnographic research offers a qualitative approach with the potential to yield detailed and comprehensive accounts of different social phenomenon (actions, behavior, interactions, and beliefs). Rather than relying on interviews or surveys, you experience the environment first hand, and sometimes as a “participant observer” which gives opportunity to gather empirical insights into social practices which are normally “hidden” from the public gaze. Reeves et al. [ 36 ] give an excellent guide to ethnography in medical education which is essential reading if you are interested in using this approach.

3. Narrative

The narrative approach weaves together a sequence of events, usually from just one or two individuals to form a cohesive story. You conduct in-depth interviews, read documents, and look for themes; in other words, how does an individual story illustrate the larger life influences that created it. Often interviews are conducted over weeks, months, or even years, but the final narrative does not need to be in chronological order. Rather it can be presented as a story (or narrative) with themes, and can reconcile conflicting stories and highlight tensions and challenges which can be opportunities for innovation.

4. Phenomenology

Phenomenology is concerned with the study of experience from the perspective of the individual, “bracketing” taken-for-granted assumptions and usual ways of perceiving. Phenomenological approaches emphasise the importance of personal perspective and interpretation. As such they are powerful for understanding subjective experience, gaining insights into people’s motivations and actions, and cutting through the clutter of taken-for-granted assumptions and conventional wisdom.

Phenomenological approaches can be applied to single cases or to selected samples. A variety of methods can be used in phenomenologically-based research, including interviews, conversations, participant observation, action research, focus meetings, and analysis of personal texts. Beware though—phenomenological research generates a large quantity data for analysis.

The phenomenological approach is used in medical education research and there are some good articles which will familiarise you with this approach [ 37 , 38 ].

5. Grounded theory

Whereas a phenomenological study looks to describe the essence of an activity or event, grounded theory looks to provide an explanation or theory behind the events. Its main thrust is to generate theories regarding social phenomena: that is, to develop higher level understanding that is “grounded” in, or derived from, a systematic analysis of data [ 39 ]. Grounded theory is appropriate when the study of social interactions or experiences aims to explain a process, not to test or verify an existing theory. Rather, the theory emerges through a close and careful analysis of the data.

The key features of grounded theory are its iterative study design, theoretical (purposive) sampling, and cycles of simultaneous data collection and analysis, where analysis informs the next cycle of data collection. In keeping with this iterative design, the sample is not set at the outset but is selected purposefully as the analysis progresses; participants are chosen for their ability to confirm or challenge an emerging theory. As issues of interest are noted in the data, they are compared with other examples for similarities and differences.

Grounded theory was first proposed by Glaser and Strauss [ 40 ] in 1967 but since then there have been many interpretations of this approach, each with their own processes and norms [ 41 , 42 , 43 ].

Beware—grounded theory is often done very badly, and numerous studies are rejected by journals because they claim to use grounded theory but do not actually do so, or do so badly.

6. Case study

Researcher Yin [ 44 ] defines the case study research method as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used. The case study method enables a researcher to closely examine the data within a specific context—for example, in a small geographical area or a very limited number of individuals as the subjects of study. Case studies explore and investigate contemporary real-life phenomenon through detailed contextual analysis of a limited number of events or conditions, and their relationships. A case study involves a deep understanding through multiple types of data sources. For example, we used case study methodology recently to explore the nature of the clinical learning environment in a general surgical unit, and used both documents and interviews as data sources. Case studies can be explanatory, exploratory, or describing an event [ 44 ] and case study design can be very open or more structured [ 45 ]. Case studies are a useful approach where the focus is to explain the complexities of real life situations.

While the five methods generally use similar data collection techniques (observation, interviews, and reviewing text—see below), the purpose of the study differentiates them.

Data collection methods

The qualitative methods most commonly used for research purposes can be classified in three broad categories: (1) interviews (individual or group), (2) observation methods, and (3) document review.

The qualitative research interview seeks to describe and gain understanding of certain themes in the life world of the subjects. Interviews can be organised one-to-one or group (focus groups) depending on the topic under study, the cultural context, and the aims of the project. Observational data collection in qualitative research involves the detailed observation of people and events to learn about behaviors and interactions in natural settings [ 46 ]. Such study designs are useful when the study goal is to understand cultural aspects of a setting or phenomenon [ 47 ], when the situation of interest is hidden, (tacit), or when subjects in the setting appear to have notably different views to other groups. Written materials or documents such as institutional records, personal diaries, and historical public documents may also serve as a valuable source of secondary data, providing insight into the lives and experiences of the group under study. For example, in one of my recent studies we used document analysis to uncover the thinking behind the design of a new medical school, then carried out interviews with “users” of the new building to explore how the intentions of the planners played out in reality. However, this is only one way of incorporating document analysis into a study: see Bowen [ 48 ] for an excellent introduction to the purpose and practicalities of document review within qualitative research.

See Dicicco-Bloom and Crabtree [ 49 ] for a useful summary of the content and process of the qualitative research interview, Creswell [ 50 ] for further discussion of the many different approaches in qualitative research and their common characteristics.

1. Data management

Qualitative research may use some form of quantification, but statistical forms of analysis are not central [ 51 ]. Instead, qualitative data analysis aims to uncover emerging themes, patterns, concepts, insights, and understandings [ 52 ]. The data are allowed to “speak for themselves” by the emergence of conceptual categories and descriptive themes. Trying to squeeze narratives into boxes (like “0” and “1”) would result in the loss of contextualisation and narrative layering. The researcher must immerse themselves in the data in order to be able to see meaningful patterns and themes, making notes as they go through the processes of data collection and analysis, and then using these notes to guide the analysis strategy.

Qualitative data has to be managed before it can be analysed—you can generate a lot of data from just a few interviews or observations! You may want to use a specialist qualitative database to facilitate data management and analysis. NVivo is a well-known qualitative data analysis software package (note that qualitative software packages enable you to make and store notes, and explanations of your codes, so you do not need to juggle bits of paper and electronic data files). These and similar databases are available commercially (i.e., at a cost) and are used widely by universities. The choice of database may be dictated by the resources of your institution, your personal preference, and/or what technical support is available locally. However, if you do not have access to qualitative data management software, then use paper and pencil: read and re-read transcripts, take notes on specifics and the bigger patterns, and label different themes with different coloured pen. You do all this in a software package anyway, as data management software does not describe or analyse your data for you. See Cleland et al. [ 53 ] for comprehensive guidance on how to use qualitative databases in education research.

Data analysis

While bearing in mind that qualitative data collection and analysis are iterative rather than linear (see earlier), Miles and Huberman [ 54 ] explain the process of qualitative data analysis as (1) data reduction (extracting the essence), (2) data display (organizing for meaning), and (3) drawing conclusions (explaining the findings).

Data analysis usually follows an inductive approach where the data are allowed to “speak for themselves” by the emergence of conceptual categories and descriptive themes. The researcher must be open to multiple possibilities or ways to think about a problem, engaging in “mental excursions” using multiple stimuli, “side-tracking” or “zigzagging,” changing patterns of thinking, making linkages between the “seemingly unconnected,” and “playing at it,” all with the intention of “opening the world to us in some way” [ 52 ]. The researcher must immerse themselves in the data in order to be able to see meaningful patterns and themes, making notes as they go through the processes of data collection and analysis, and then using these notes to guide the analysis strategy and the development of a coding framework.

In this way, good qualitative research has a logical chain of reasoning, multiple sources of converging evidence to support an explanation, and rules out rival hypotheses with convincing arguments and solid data. The wider literature and theory are used to derive analytical frameworks as the process of analysis develops and different interpretations of the data are likely to be considered before the final argument is built. For example, one of our own studies aimed to explore how widening access policy is translated and implemented at the level of individual medical schools [ 4 ]. Data was collected via individual interviews with key personnel. We initially conducted a primary level thematic analysis to determine themes. After the themes emerged, and following further team discussion, we explored the literature, identified and considered various theories, in some depth, before identifying the most appropriate theory or conceptual lens for a secondary, theory-driven analysis.

There are some excellent text books which discuss qualitative data analysis in detail [ 35 , 55 ].

Judging the quality of research

There are various criteria by which you can judge the quality of qualitative research. These link to efforts by the research team to consider their findings. The most common ways of doing so are triangulation, respondent validation, reflexivity, detail and process, and fair dealing [ 56 ] (but see also Varpio et al. [ 57 ] for a detailed discussion of the limitations of some of these methods).

Triangulation compares the results from either two or more different methods of data collection (for example, interviews and observation) or, more simply, two or more data sources (for example, interviews with different people). The researcher looks for patterns of convergence to develop or corroborate an overall interpretation. This is as a way of ensuring comprehensiveness. Respondent validation, or “member checking,” includes techniques in which the investigator’s account is compared with those of the research subjects to establish the level of correspondence between the two sets. Study participants’ reactions to the analyses are then incorporated into the study findings. Providing a clear account of the process of data collection and analysis is important. By the end of the study, it should be possible to provide a clear account of how early, simple coding evolved into more sophisticated coding structures and thence into clearly defined concepts and explanations for the data collected. Reflexivity is discussed earlier but in terms of analysis reflexivity means sensitivity to the ways in which the researcher and the research process have shaped the collected data, including the role of prior assumptions and experience. These two points address credibility, whether the study has been conducted well and the findings seem reasonable. It is important to pay attention to “negative cases,” data that contradict, or seem to contradict, the emerging explanation of the phenomena under study. These can be a very useful source of information in terms of refining the analysis and thinking beyond the obvious. The final technique is to ensure that the research design explicitly incorporates a wide range of different perspectives. In practice this can mean presenting data from a wide range of diverse participants. A very practical point is worth mentioning here—any reviewer will want to see quotes labelled in some way; for example, P11FFG2 would be participant 11, female, focus group 2). This helps the reader see that your data does not just represent the view of one or two people, but that there is indeed some sort of pattern or commonality to report.

Guba and Lincoln [ 58 ] provide the following criteria for judging qualitative research: credibility, transferability, dependability, and confirmability. I direct you to the original resource and to a very good explanation of these criteria in Mann and MacLeod [ 8 ].

Qualitative research is very important in educational research as it addresses the “how” and “why” research questions and enables deeper understanding of experiences, phenomena, and context. Qualitative research allows you to ask questions that cannot be easily put into numbers to understand human experience. Getting at the everyday realities of some social phenomenon and studying important questions as they are really practiced helps answer big questions. To do so, you need to understand the philosophical stance of qualitative research and work from this to develop the research question, study design, data collection methods, and data analysis.

Qualitative and quantitative research in the humanities and social sciences: how natural language processing (NLP) can help

• Published: 23 September 2021
• Volume 56 , pages 2751–2781, ( 2022 )

Cite this article

• Roberto Franzosi   ORCID: orcid.org/0000-0001-8367-5190 1 ,
• Wenqin Dong 2 &
• Yilin Dong 2  

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The paper describes computational tools that can be of great help to both qualitative and quantitative scholars in the humanities and social sciences who deal with words as data. The Java and Python tools described provide computer-automated ways of performing useful tasks: 1. check the filenames well-formedness; 2. find user-defined characters in English language stories (e.g., social actors, i.e., individuals, groups, organizations; animals) (“find the character”) via WordNet; 3. aggregate words into higher-level aggregates (e.g., “talk,” “say,” “write” are all verbs of “communication”) (“find the ancestor”) via WordNet; 4. evaluate human-created summaries of events taken from multiple sources where key actors found in the sources may have been left out in the summaries (“find the missing character”) via Stanford CoreNLP POS and NER annotators; 5. list the documents in an event cluster where names or locations present close similarities (“check the character’s name tag”) using Levenshtein word/edit distance and Stanford CoreNLP NER annotator; 6. list documents categorized into the wrong event cluster (“find the intruder”) via Stanford CoreNLP POS and NER annotators; 7. classify loose documents into most-likely event clusters (“find the character’s home”) via Stanford CoreNLP POS and NER annotators or date matcher; 8. find similarities between documents (“find the plagiarist”) using Lucene. These tools of automatic data checking can be applied to ongoing projects or completed projects to check data reliability. The NLP tools are designed with “a fourth grader” in mind, a user with no computer science background. Some five thousand newspaper articles from a project on racial violence (Georgia 1875–1935) are used to show how the tools work. But the tools have much wider applicability to a variety of problems of interest to both qualitative and quantitative scholars who deal with text as data.

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On PEA see Koopmans and Rucht ( 2002 ) and (Hutter 2014 ); on PEA and its more rigorous methodological counterpart rooted in a linguistic theory of narrative and rhetoric, Quantitative Narrative Analysis (QNA), see Franzosi ( 2010 ).

See, for instance, Franzosi’s PC-ACE (Program for Computer-Assisted Coding of Events) at www.pc-ace.com (Franzosi 2010 ).

For recent surveys, see Evans and Aceves ( 2016 ), Edelmann et al. ( 2020 ).

The GitHub site will automatically install not only all the NLP Suite scripts but also Python and Anaconda required to run the scripts. It also provides extensive help on how to download and install a handful of external software required by some of the algorithms (e.g., Stanford CoreNLP, WordNet). The goal is to make it as easy as possible for non-technical users to take advantage of the tools with minimal investment.

We rely on the Python package openpyxl and ad hoc functions.

The newspaper collections found in Chronicling America of the Library of Congress ( http://chroniclingamerica.loc.gov/newspapers/ ), the Digital Library of Georgia ( http://dlg.galileo.usg.edu/MediaTypes/Newspapers.html?Welcome ), The Atlanta Constitution, Proquest, Readex.

Multiple cross-references are also possible, whereby a document deals with several different events.

Contrary to some protest event projects based on a single newspaper source (e.g., The New York Times in the “Dynamics of Collective Action, 1960–1995” project that involved several social scientists, notably, Doug McAdam, John McCarthy, Susan Olzak, Sarah Soule, and led to dozens of influential publications; see for all McAdam and Su 2002 ), the Georgia lynching project is based on multiple newspaper sources for each event.

Franzosi reports 1,600 distinct entries for subjects and objects and 7,000 for verbs for one of his projects (Franzosi 2010 : 93); similar figures are reported by Ericsson and Simon ( 1996 : 265–266) and Tilly ( 1995 : 414–415).

The most up-to-date numbers of terms are given in https://wordnet.princeton.edu/documentation/wnstats7wn .

A common critique of WordNet is that WordNet is better suited to account for concrete concepts than for abstract concepts. It is much easier to create hyponyms/hypernym relationships between “conifer” as a type of “tree”, a “tree” as a type of “plant”, and a “plant” as a type of “organism”. Not so easy to classify emotions like “fear” or “happiness” into hyponyms/hypernym relationships.

https://projects.csail.mit.edu/jwi/

The WordNet databases comprises both single words or combinations of two or more words that typically come together with a specific meaning (collocations, e.g., coming out, shut down, thumbs up, stand in line, customs duty). Over 80% of terms in the WordNet database are collocations, at least at the time of Miller et al.’s Introduction to WordNet manual (1993, p. 2). For the English language (but WordNet is available for some 200 languages) the database contains a very large set of terms. The most up-to-date numbers of terms are given in https://wordnet.princeton.edu/documentation/wnstats7wn .

Data aggregation is often referred to as “data reduction” in the social sciences and as “linguistic categorization” in linguistics (on linguistic categorization, see Taylor 2004 ; on verbs classification, Levin 1993 ; see also Franzosi 2010 : 61).

On the way up through the hierarchy, the script relies on the WordNet concepts of hypernym – the generic term used to designate a whole class of specific instances (Y is a hypernym of X if X is a (kind of) Y) – and holonym – the name of the whole of which the meronym names is a part. Y is a holonym of X if X is a part of Y.

Collocations are sets of two or more words that are usually together for a complete meaning, e.g., “coming out,” “sunny side up”. Over 80% of terms in the WordNet database are collocations, at least at the time of Miller et al.’s Introduction to WordNet manual (1993, p. 2). For the English language (but WordNet is available for some 200 languages) the database contains a very large set of terms. The most up-to-date numbers of terms in each category are given in https://wordnet.princeton.edu/documentation/wnstats7wn

The 25 top noun synsets are: act, animal, artifact, attribute, body, cognition, communication, event, feeling, food, group, location, motive, object, person, phenomenon, plant, possession, process, quantity, relation, shape, state, substance, time.

The 15 top verb synsets are: body, change, cognition, communication, competition, consumption, contact, creation, emotion, motion, perception, possession, social, stative, weather.

Unfortunately, there is no easy way to aggregate at levels lower than the top synsets. Wordnet is a linked graph where each node is a synset and synsets are interlinked by means of conceptual-semantic and lexical relations. In other words, it is not a simple tree structure: there is no way to tell at which level the synset is located at. For example, the synset “anger” can be traced from top level synset “feeling” and follows the path: feeling—> emotion—> anger. But it can also be traced from top level synset “state” and follows the path: state—> condition—> physiological condition—> arousal—> emotional arousal—> anger. In the first case, “anger” is at level 3 (assuming “feeling” and or other top synsets are level 1). In the second case, “anger” is at level 6. Programmatically, if one gives users more freedom to control the level of aggregating up, it is hard to build a user-friendly communication protocol. If the user wants to aggregate up to level 3 (two levels below the top synset), then should “anger” be considered as a level 3 synset? Does the user want “anger” to be considered as a level 3 synset? Since there is no clear definition of how far away a synset is from the root (top synsets), our algorithm aggregates all the way up to root.

Suppose that you wish to aggregate the verbs in your corpus under the label “violence.” WordNet top synsets for verbs do not include “violence” as a class. Verbs of violence may be listed under body, contact, social. You could use the Zoom IN/DOWN widget of Figure 24 to get a list of verbs in these top synsets, then manually go through the list to select only the verbs of violence of interest. That would mean go through manually the list of 956 verbs in the body class (e.g., to find there the verb “attack,” among others), the 2515 verbs of contact (e.g., to find there the verb “wrestle”), and the 1688 verbs of social (e.g., to find there the verb “abuse”). In total, 5159 distinct verbs. A restricted domain, for example newspaper articles of lynching, may have many fewer distinct verbs, indeed 2027, extracted using the lemma of the POS annotator for all the VB* tags. Whether using the WordNet dictionary (a better solution if the list of verbs of violence has to be used across different corpora) or the POS distinct verb tags, the dictionary list can then be used to annotate the documents in the corpus via the NLP Suite dictionary annotator GUI.

Current computational technology makes available a different approach to creating summaries: an automatic approach where summaries are generated automatically by a computer algorithm, rather than a human (Gambhir and Gupta 2017 ; Lloret and Palomar 2012 ; Nenkova and McKeown 2012 ).

We use the word “compilation”, rather than “summary”, since, by and large, we maintained the original newspaper language (e.g., the word “negro”, rather than “African American”) and original story line, however contrived the story may have appeared to be.

https://stanfordnlp.github.io/CoreNLP/ Manning et al. ( 2014 ).

More specifically, for locations, the NER tags used are: City, State_or_Province, Country. Several other NER values are also recognized and tagged (e.g., Numbers, Percentages, Money, Religion), but they are irrelevant in this context.

The column “List of Documents for Type of Error” may be split in several columns depending upon the number of documents found in error.

The algorithm can process all or selected NER values, comparing the associated word values either within a single event subdirectory or across all subdirectories (or all the files listed in a directory, for that matter).

We calculated the relativity index by using cosine similarity (Singhal 2001 ). We use the two list of NN, NNS, Location, Date, Person, and Organization from the j doc (L1) and from all other j-1 docs (L2) and compute cosine similarity between the two lists. We construct a vector from each list by mapping the word count onto each unique word. Then, relativity index is calculated as the cosine similarity between two vectors and n is the count of total unique words. For instance, L1 is {Alice: 2, doctor: 3, hospital: 1}, and L2 is {Bob:1, hospital: 2}. If we fix the order of all words as {Alice, doctor, hospital, Bob}, then the first vector (V1) is (2, 3, 1, 0), the second vector (V2) is (0, 0, 2, 1), and the length n of the vector is 4. The relativity is the dot product of two vectors divided by the product of two vector lengths. Documents with index of relativity significantly lower than the rest of the cluster are signalled as unlikely to belong to the cluster.

\({\text{relativity}}\;{\text{index}} = \frac{{\sum\nolimits_{i = 1}^{n} {\left( {V1_{i} V2_{i} } \right)} }}{{\sqrt {\sum\nolimits_{i = 1}^{n} {V1_{i}^{2} } } \sqrt {\sum\nolimits_{i = 1}^{n} {V2_{i}^{2} } } }}\)

The relativity index ranges from 0 to 1, where 0 means two documents are totally different, and 1 means two documents have exactly the same list of NN, NNS, Location, Date, Person, and Organization.

The bar chart displays the distribution of most frequent threshold index values as intervals, with most records in the 0.25 ~ 0.29 interval.

It should be noted that the use of the words plagiarism and plagiarist in this context should be taken with a grain of salt. First, the data do not tell us anything about who copied whom, but only that the two different newspapers shared content, wholly or in part; furthermore, the shared content may well have come from an unacknowledged wire service (on the development and spread of news wire services in the United States during the second half of the nineteenth century, see Brooker-Gross 1981 ; on computational tools for plagiarism and authorship attribution, see, for instance, Stein et al. 2011 ).

http://lucene.apache.org/core/downloads.html . For a summary of approaches to document similarities, see Forsyth and Sharoff ( 2014 ).

Other approaches are also available. After all, determining document similarity has been a major research area due to its wide application in information retrieval, document clustering, machine translation, etc. Existing approaches to determine document similarity can be grouped into two categories: knowledge-based similarity and content-based similarity (Benedetti et al., 2019 ).

Knowledge-based similarity approaches extract information from other sources to supplement the corpus, so as to draw from more document features to analyze. For example, Explicit Semantic Analysis (ESA) (Gabrilovich and Markovitch 2007 ) represents documents in high dimensional vectors based on the features extracted from both original articles and Wikipedia articles. Then, similarity of documents is calculated using vector space comparison algorithm. Since our main focus in this work is to detect plagiarism among texts in the same corpus, knowledge-based similarity approaches are not very fruitful.

Content-based similarity approaches focus on using only textual information contained in documents. Popular proposed techniques in this fields are Vector Space Models (Turney and Pantel 2010 ), probabilistic models such as Okapi BM-25 (Robertson and Zaragoza 2009 ). These methods all transform documents into some form of representations, and then either do a vector space comparison or query search match on the constructed representations.

document_duplicates.txt.

Users can specify different spans of temporal aggregation (e.g., year, quarter/year, month/year).

In this specific application, documents are newspapers where document name refers to the name of the paper (e.g., The New York Times) and document instance refers to a specific newspaper article (e.g., The New York Times_12-11-1912_1, referring to a The New York Times of December 11, 1912 on page 1). But the document name could refer to an ethnographic interview with document instance referring to an interviewer’s ID (by name or number), an interview’s location, time, or interviewee (by name or ID number).

The numbers in each row of the table add up to approximately the total number of newspaper articles in the corpus. This number of not exact due to the way the Lucene function “find top similar documents” computes similar documents with discrepancies numbering in the teens.

On the specific topic of lynching, see, for instance, the quantitative work by Beck and Tolnay ( 1990 ) or Franzosi et al. ( 2012 ) and the more qualitative work by Brundage ( 1993 ).

Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 163–222. Springer, Boston (2012)

Chapter   Google Scholar  

Beck, E.M., Tolnay, S.: ‘The killing fields of the deep south: the market for cotton and the lynching of blacks, 1882–1930.’ Am. Sociol. Rev. 55 , 526–539 (1990)

Article   Google Scholar  

Beck, E.M., Tolnay, S.E.: Confirmed inventory of southern lynch victims, 1882–1930. Data file available from authors (2004).

Benedetti, F., Beneventano, D., Bergamaschi, S., Simonini, G.: Computing inter document similarity with Context Semantic Analysis. Inf. Syst. 80 , 136–147 (2019). https://doi.org/10.1016/j.is.2018.02.009

Białecki, A., Muir, R., & Ingersoll, G.: "Apache Lucene 4." SIGIR 2012 Workshop on Open Source Information Retrieval . August 16, 2012, Portland, OR, USA (2012).

Brundage, F.: Lynching in the New South: Georgia and Virginia, 1880–1930. University of Illinois Press, Urbana (1993)

Google Scholar  

Johansson, J., Borg, M., Runeson, P., Mäntylä, M.V.:A replicated study on duplicate detection: using Apache Lucene to search among android defects. In: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, 8. ACM (2014)

Brooker-Gross, S.R.: News wire services in the nineteenth-century United States. J. Hist. Geogr. 7 (2), 167–179 (1981)

Cooper, J.W., Coden, A.R. Brown, E.W.: Detecting similar documents using salient terms. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management , 245–251 (2002)

Edelmann, A., Wolff, T., Montagne, D., Bail, C.A.: Computational social science and sociology. Ann. Rev. Sociol. 46 , 61–81 (2020)

Ericsson, K.A., Herbert, S.: Protocol Analysis: Verbal Reports as Data, 2nd edn. MIT Press, Cambridge, MA (1996)

Evans, J.A., Aceves, P.: Machine translation: mining text for social theory. Ann. Rev. Sociol. 42 , 21–50 (2016)

Fellbaum, C. (ed.): WordNet. An Electronic Lexical Database. MIT Press, Cambridge, MA (1998)

Forsyth, R.S., Sharoff, S.: Document dissimilarity within and across languages: a benchmarking study. Liter. Linguistic Comput 29 (1), 6–22 (2014)

Franzosi, R.: Quantitative Narrative Analysis, vol. 162. Sage, Thousand Oaks, CA (2010)

Book   Google Scholar  

Franzosi, R., De Fazio, G., Vicari, S.: Ways of measuring agency: an application of quantitative narrative analysis to lynchings in Georgia (1875–1930). Sociol. Methodol. 42 (1), 1–42 (2012)

Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJcAI 7 , 1606–1611 (2007)

Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. Artif. Intell. Rev. 47 , 1–66 (2017)

Grimm, J., Grimm, W.: [1812, 1857]. The original folk and fairy tales of the brothers Grimm: The Complete First Edition. [ Kinder- und Hausmärchen. Children’s and Household Tales ]. Translated and Edited by Jack Zipes. Princeton, NJ: Princeton University Press (2014)

Hutter, S.: Protest event analysis and its offspring. In: Donatella della Porta (ed.) Methodological Practices in Social Movement Research. Oxford: Oxford University Press, pp. 335–367 (2014)

Jacobs, J.: English fairy tales (Collected by Joseph Jacobs, Illustrated by John D. Batten) . London: David Nutt (1890)

Klandermans, B., Staggenborg, S. (eds.): Methods of Social Movement Research. University of Minnesota Press, Minneapolis (2002)

Koopmans, R., Rucht, D.: Protest event analysis. In: Klandermans, Bert, Staggenborg, Suzanne (eds.) Methods of Social Movement Research, pp. 231–59. University of Minnesota Press, Minneapolis (2002)

Kowsari, K., Meimandi, K.J., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 2019 (10), 150 (2019)

Labov, W.: Language in the Inner City. University of Pennsylvania Press, Philadelphia (1972)

Lansdall‐Welfare, T., Sudhahar, S., Thompson, J., Lewis, J., FindMyPast Newspaper Team, and Cristianini, N.: Content analysis of 150 years of british periodicals. Proceedings of the National Academy of Sciences (PNAS), PNAS , Published online January 9, 2017 E457–E465 (2017)

Lansdall-Welfare, T., Cristianini, N.: History playground: a tool for discovering temporal trends in massive textual corpora. Digit. Scholar. Human. 35 (2), 328–341 (2020)

Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Doklady Akademii Nauk SSSR, 163(4):845–848, 1965 (Russian). English translation in Soviet Physics Doklady , 10(8):707–710, 1966. (Doklady is Russian for "Report". Sometimes transliterated in English as Doclady or Dokladi.) (1966)

Levin, B.: English Verb Classes and Alternations. The University of Chicago Press, Chicago (1993)

Lloret, E., Palomar, M.: Text summarisation in progress: a literature review. Artif. Intell. Rev. 37 , 1–41 (2012)

MacEachren, A.M., Roth, R.E., O'Brien, J., Li, B., Swingley, D., and Gahegan, M.: Visual semiotics & uncertainty visualization: an empirical study. IEEE Transactions on Visualization and Computer Graphics , Vol. 18, No. 12, December 2012 (2012)

Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J. and McClosky, D.: The stanford CoreNLP natural language processing toolkit. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations , pp. 55–60 (2014)

McAdam, D., Yang, Su.: The war at home: antiwar protests and congressional voting, 1965–1973. Am. Sociol. Rev. 67 (5), 696–721 (2002)

McCandless, M., Hatcher, E., Gospodnetic, O.: Lucene in Action, Second Edition Covers Apache Lucene 3.0. Manning Publications Co, Greenwich, CT (2010)

Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38 (11), 39–41 (1995)

Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3 (4), 235–244 (1990)

Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Aggarwal, C.C., Cheng, X.Z. (eds.) Mining Text Data, pp. 43–76. Springer, Boston (2012)

Murchú, T.Ó., Lawless, S.: The problem of time and space: the difficulties in visualising spatiotemporal change in historical data. Proc. Dig. Human. 7 (8), 12 (2014)

Panitch, L.: Corporatism: a growth industry reaches the monopoly stage. Can. J. Polit. Sci. 21 (4), 813–818 (1988)

Robertson, S., Zaragoza, H.: The probabilistic relevance framework BM25 and beyond. Found. Trends® Inf Retr. 3 (4), 333–389 (2009).

Singhal, A.: Modern information retrieval: a brief overview. Bull. IEEE Comput. Soc. Tech. Comm. Data Eng. 24 (4), 35–43 (2001)

Stein, B., Lipka, N., Prettenhofer, P.: Plagiarism and authorship analysis. Lang. Resour. Eval. 45 (1), 63–82 (2011)

Taylor, J.R.: Linguistic Categorization. Oxford University Press, Oxford (2004)

Tilly, C.: Popular Contention in Great Britain, 1758–1834. Harvard University Press, Cambridge, MA (1995)

Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Int. Res. 37 (1), 141–188 (2010)

Zhang, H., Pan, J.: CASM: a deep-learning approach for identifying collective action events with text and image data from social media. Sociol. Methodol. 49 (1), 1–57 (2019)

Zhang, Y., Li, J.L.: Research and improvement of search engine based on Lucene. Int. Conf. Intell. Human-Mach. Syst. Cybern. 2 , 270–273 (2009)

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See Figs. 23 , 24 and 25

Screenshot of the Graphical User Interface (GUI) for the filename checker

Graphical User Interface (GUI) for WordNet options

Graphical User Interface (GUI) for Word Similarities

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Franzosi, R., Dong, W. & Dong, Y. Qualitative and quantitative research in the humanities and social sciences: how natural language processing (NLP) can help. Qual Quant 56 , 2751–2781 (2022). https://doi.org/10.1007/s11135-021-01235-2

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Accepted : 02 September 2021

Published : 23 September 2021

Issue Date : August 2022

DOI : https://doi.org/10.1007/s11135-021-01235-2

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