qualitative and quantitative research methods sociology

Sociological Research Methods: Qualitative and Quantitative Methods

Research methods and analysis of sociology dealt with techniques to obtain information in a vivid form.

Sociologists Redman and Mory explained research work as a systematic way to earn new knowledge or say angle towards anything. For example, after a research work, various developments can be seen.

Research methods are categorized into Qualitative and Quantitative methods .

Quantitative methods included data structures, mathematical formulas, postulates, analysis by pie charts, graphical representations, Co-relation, Regression, etc. The methods used in Quantitative research will be studied in detail below.

Positivists majorly depend upon this method because they think it is the most convenient and efficient way to see society and its problems.  For example, the rate of sex ratio or the number of rape happening in a particular area makes sociologists see the present scenario of the society.

Durkheim observed the basis of division of labour and Weber tried to link the relation between capitalist and exploited countries. This method is still used by many sociologists for letting the world know about differences. For example, Michael Mann compared how every country differs when it comes to power and dominance. Devine showed the condition of workers in different time periods.

Qualitative Methods are those methods which depend on the theories of Interactionism Theories. For example people way of talking under different circumstances studied by a researcher. The result will be completely based on the way the researcher perceives everything. The various methods of a Qualitative method are studied below.

This method was one step up-gradation to field methods and Participant Observation. This observation also included a third party involvement whose perception cannot fall into the claws of a biased nature. For example, even if a researcher tries to complete experiment, he will not totally drench himself into the perception of the participant, thus a third person who will see the whole activity without any judgment will yield better results. For example in cricket matches, apart from umpires, a proper video is taken to see whether the player is out or not. This makes the judgment fair enough for everybody. In simple words, participant and researchers are not aware of the fact that they are being observed which accounts for natural reactions.

TECHNIQUES OF DATA COLLECTION

Data collection is mainly stored in two ways, primary resources , and secondary resources .

Primary Resources are the data which are obtained by researchers, for example through personal or telephonic interviews, participant behaviour by keenly observing them or asking them a set of questions.

MORE METHODS OF QUALITATIVE AND QUANTITATIVE ANALYSIS:-

Inactive observation, the researcher is also a part of an analysis. For example, he will take part in a game and will play fairly at his part.

Uncontrolled observations are those observations in which neither researcher nor the people under observation stop the process of analysis. They are being adaptive to any situation no matter what results can be obtained.

There is another type called a Mixed Observation type. In these methods, extremities are found. Either the researcher is totally drenching in the activity or will be observing every bit in solitude. It is also known as Quasi Participant Observation.

This method involves a panel of interviewers and applicants. For example, in any placement drive, a panel is set up and they took a massive amount of information about the applicants by asking them many questions. Much information about their personality, IQ, confidence, abilities is judged in a matter of some minutes. The interviews can be of many types viz. formal, informal, solo or group.

A questionnaire is a set of questions designed in a format which can be solved by only those who can read and write. Thus the biggest disadvantage of this method is that it cannot be fulfilled by everybody. The sole purpose of this method is storing answers and due to same questions, best answers manage to secure the position.

Continue Reading → Variable,Sampling,Hypothesis,Reliability & Validity

Sociology Group

Research Methods

Table of Contents

Last Updated on October 13, 2023 by Karl Thompson

Sociologists use a range of quantitative and qualitative, primary and secondary social research methods to collect data about society.

The main types of research method are:

This page provides links to more in depth posts on all of the above research methods. It has primarily been written for students studying the A Level Sociology AQA 7192 specification, and incorporates Methods in the Context of Education.

Research Methods at a Glance – Key Concepts  

Research Methods Top Ten Key Concepts – start here if you’re all at sea – includes simple explanations of terms such as validity, reliability, representativeness, Positivism and Interpretivism .

Research Methods A-Z Glossary – a more comprehensive index of the key terms you need to know for AS and A Level Sociology .

An Introduction to Research Methods

Without research methods there is no sociology!

Research Methods in Sociology – An Introduction  – d etailed class notes covering the basic types of research method available to sociologists such as social surveys, interviews, experiments, and observations

Positivism and Interpretivism – Positivists generally prefer quantitative methods, Interpretivists prefer qualitative methods – this post consists of brief summary revision notes and revision diagrams outlining the difference between positivist and interpretivist approaches to social research. 

Outline and explain two practical problems which might affect social research (10) –  A model answer to this exam question, which could appear on either paper 7191 (1) or 7191 (3). 

Primary Quantitative Research Methods

  social surveys.

The advantages and disadvantages of social surveys in social research  –  detailed class notes covering the theoretical, practical and ethical strengths and limitations of social surveys. Generally, surveys are preferred by positivists and good for simple topics, but not so good for more complex topics which require a ‘human touch’ .

Structured Interviews in Social Research – Interviews are effectively one of the means of administering social surveys. This post covers the different contexts (types) of structured interview, and the stages of doing them. It also looks at the strengths, limitations and criticisms.

Experiments

Laboratory Experiments in Sociology   – detailed class notes on the strengths and limitations of laboratory experiments. Sociologists don’t generally use lab experiments, but examiners seem to ask questions about them more than other methods – one hypothesis for why is that sociology examiners have a burning hatred of teenagers. 

Longitudinal Studies

Longitudinal Studies – These are interval studies designed to explore changes over a long period of time. Researchers start with a sample and keep going back to that same sample periodically – say every year, or every two years, to explore how and why changes occur.

What Makes a Good Life ? – Lessons from a Longitudinal Study – This is one of the longest running Longitudinal studies in the world – the respondents were in their 20s when it started, now those who are still alive are in their 80s.

Primary Qualitative Research Methods

Primary qualitative research methods tend to be favoured by Interpretivists as they allow respondents to speak for themselves, and should thus yield valid data. However, because qualitative methods tend to involve the researcher getting more involved with the respondents, there is a risk that the subjective views of the researcher could interfere with the results, which could compromise both the validity and reliability of such methods.

Participant Observation

The strengths and limitations of covert participant observation – sociologists don’t generally use covert participant observation because of the ethical problem of deception means they can’t get funding. This methods is more commonly used by journalists doing investigative reporting, or you could even say undercover police officers use it, and you can use these examples to illustrate the advantages and disadvantages of this method.

Interviews in Social Research  –  This post consists of detailed class notes focusing strengths and limitations of mainly unstructured interviews, which are like a guided conversation that allow respondents the freedom to speak for themselves.

Secondary Research Methods

Official statistics.

Cross National Comparisons – Comparing data across countries using official statistics can provide insight into the causes of social problems such as poverty, and war and conflict. This post looks at how you might go about doing this and the strengths and limitations of this kind of research.

Secondary Qualitative Data

Content Analysis of the Media in Social Research  –  class notes covering formal content (quantitative) analysis and semiology .

Autobiographies in social research – Autobiographies are popular with the British public, but how useful are they as sources of data for the social researcher?

Sociology, Science and Value Freedom (Part of A2 Theory and Methods)

Sociology and Value Freedom  – Detailed class notes .

Methods in Context – Research Methods Applied to Education

Non-Participant Observation in Education  –  focusing on OFSTED inspections, as these are probably the most commonly used of all methods in education .

Focus on the AS and A Level Exams

Research Methods Practice Questions for A-level Sociology – you will get a 10 mark question on both papers SCLY1 and SCLY3 most likely asking you to ‘outline and explain’ the strengths and limitations of any of the main research methods. This post outlines some of the many variations.

Methods in Context Essay Template  – a suggested gap fill essay plan on how to answer these challenging ‘applied research methods’ questions.

Using Participant Observation to research pupils with behavourial difficulties (20) – a model answer for this methods in context style of essay.

Other Relevant Posts

How old are twitter users? – applied sociology – illustrates some of the problems us using social media to uncover social trends.

Theory and Methods A Level Sociology Revision Bundle 

If you like this sort of thing, then you might like my Theory and Methods Revision Bundle – specifically designed to get students through the theory and methods sections of  A level sociology papers 1 and 3.

For better value I’ve bundled all of the above topics into six revision bundles , containing revision notes, mind maps, and exam question and answers, available for between £4.99 and £5.99 on Sellfy .

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2.2 Research Methods

Learning objectives.

By the end of this section, you should be able to:

• Recall the 6 Steps of the Scientific Method
• Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis.
• Explain the appropriateness of specific research approaches for specific topics.

Sociologists examine the social world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary data analysis , or use of existing sources. Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use. When you are conducting research think about the best way to gather or obtain knowledge about your topic, think of yourself as an architect. An architect needs a blueprint to build a house, as a sociologist your blueprint is your research design including your data collection method.

When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher wouldn’t stroll into a crime-ridden neighborhood at midnight, calling out, “Any gang members around?”

Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviors, early education, or the Ku Klux Klan. Researchers can’t just stroll into prisons, kindergarten classrooms, or Klan meetings and unobtrusively observe behaviors or attract attention. In situations like these, other methods are needed. Researchers choose methods that best suit their study topics, protect research participants or subjects, and that fit with their overall approaches to research.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviors and opinions, often in the form of a questionnaire or an interview. The survey is one of the most widely used scientific research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point, most people in the United States respond to some type of survey. The 2020 U.S. Census is an excellent example of a large-scale survey intended to gather sociological data. Since 1790, United States has conducted a survey consisting of six questions to received demographical data pertaining to residents. The questions pertain to the demographics of the residents who live in the United States. Currently, the Census is received by residents in the United Stated and five territories and consists of 12 questions.

Not all surveys are considered sociological research, however, and many surveys people commonly encounter focus on identifying marketing needs and strategies rather than testing a hypothesis or contributing to social science knowledge. Questions such as, “How many hot dogs do you eat in a month?” or “Were the staff helpful?” are not usually designed as scientific research. The Nielsen Ratings determine the popularity of television programming through scientific market research. However, polls conducted by television programs such as American Idol or So You Think You Can Dance cannot be generalized, because they are administered to an unrepresentative population, a specific show’s audience. You might receive polls through your cell phones or emails, from grocery stores, restaurants, and retail stores. They often provide you incentives for completing the survey.

Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel, think, and act—or at least how they say they feel, think, and act. Surveys can track preferences for presidential candidates or reported individual behaviors (such as sleeping, driving, or texting habits) or information such as employment status, income, and education levels.

A survey targets a specific population , people who are the focus of a study, such as college athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes. Most researchers choose to survey a small sector of the population, or a sample , a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. As a result, a Gallup Poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people.

After selecting subjects, the researcher develops a specific plan to ask questions and record responses. It is important to inform subjects of the nature and purpose of the survey up front. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument, which is a means of gathering the information.

A common instrument is a questionnaire. Subjects often answer a series of closed-ended questions . The researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question. This kind of questionnaire collects quantitative data —data in numerical form that can be counted and statistically analyzed. Just count up the number of “yes” and “no” responses or correct answers, and chart them into percentages.

Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” or checkbox options. These types of inquiries use open-ended questions that require short essay responses. Participants willing to take the time to write those answers might convey personal religious beliefs, political views, goals, or morals. The answers are subjective and vary from person to person. How do you plan to use your college education?

Some topics that investigate internal thought processes are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of personal explanation is qualitative data —conveyed through words. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of in-depth material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and it is a way of conducting surveys on a topic. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly.

Questions such as “How does society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. The researcher will also benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Surveys often collect both quantitative and qualitative data. For example, a researcher interviewing people who are incarcerated might receive quantitative data, such as demographics – race, age, sex, that can be analyzed statistically. For example, the researcher might discover that 20 percent of incarcerated people are above the age of 50. The researcher might also collect qualitative data, such as why people take advantage of educational opportunities during their sentence and other explanatory information.

The survey can be carried out online, over the phone, by mail, or face-to-face. When researchers collect data outside a laboratory, library, or workplace setting, they are conducting field research, which is our next topic.

Field Research

The work of sociology rarely happens in limited, confined spaces. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people think and behave. It seeks to understand why they behave that way. However, researchers may struggle to narrow down cause and effect when there are so many variables floating around in a natural environment. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables. Indeed, much of the data gathered in sociology do not identify a cause and effect but a correlation .

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and fingers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see whether anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a writer, or a sociologist, will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. A researcher might work as a waitress in a diner, experience homelessness for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in analyzing data and generating results.

In a study of small towns in the United States conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in U.S. towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised the purpose of their study.

This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd & Lynd, 1929).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behavior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job.

Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book and describe what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study . To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

The book she wrote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms.

Ethnography

Ethnography is the immersion of the researcher in the natural setting of an entire social community to observe and experience their everyday life and culture. The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a social group.

An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record data, and collate the material into results.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith (1990), institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male- dominated societies and power structures. Smith’s work is seen to challenge sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from the male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada n.d.). Smith’s three major works explored what she called “the conceptual practices of power” and are still considered seminal works in feminist theory and ethnography (Fensternmaker n.d.).

Sociological Research

The making of middletown: a study in modern u.s. culture.

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000) as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minorities or outsiders—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds objectively described what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. As a result, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that Muncie was divided into business and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was an emerging material reality of the 1920s.

As the Lynds worked, they divided their manuscript into six chapters: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

Middletown: A Study in Modern American Culture was not only published in 1929 but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (Caplow, Hicks, & Wattenberg. 2000).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times. Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data was important—and interesting—to the U.S. public.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that while offering depth on a topic, it does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can contribute tremendous insight. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” growth and nurturing. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be obtained by any other method.

Experiments

You have probably tested some of your own personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis.

One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach.

There are two main types of experiments: lab-based experiments and natural or field experiments. In a lab setting, the research can be controlled so that more data can be recorded in a limited amount of time. In a natural or field- based experiment, the time it takes to gather the data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher.

As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens (cause), then another particular thing will result (effect). To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables.

Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group. The experimental group is exposed to the independent variable(s) and the control group is not. To test the benefits of tutoring, for example, the sociologist might provide tutoring to the experimental group of students but not to the control group. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record of a student, for example.

And if a researcher told the students they would be observed as part of a study on measuring the effectiveness of tutoring, the students might not behave naturally. This is called the Hawthorne effect —which occurs when people change their behavior because they know they are being watched as part of a study. The Hawthorne effect is unavoidable in some research studies because sociologists have to make the purpose of the study known. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985).

A real-life example will help illustrate the process. In 1971, Frances Heussenstamm, a sociology professor at California State University at Los Angeles, had a theory about police prejudice. To test her theory, she conducted research. She chose fifteen students from three ethnic backgrounds: Black, White, and Hispanic. She chose students who routinely drove to and from campus along Los Angeles freeway routes, and who had had perfect driving records for longer than a year.

Next, she placed a Black Panther bumper sticker on each car. That sticker, a representation of a social value, was the independent variable. In the 1970s, the Black Panthers were a revolutionary group actively fighting racism. Heussenstamm asked the students to follow their normal driving patterns. She wanted to see whether seeming support for the Black Panthers would change how these good drivers were treated by the police patrolling the highways. The dependent variable would be the number of traffic stops/citations.

The first arrest, for an incorrect lane change, was made two hours after the experiment began. One participant was pulled over three times in three days. He quit the study. After seventeen days, the fifteen drivers had collected a total of thirty-three traffic citations. The research was halted. The funding to pay traffic fines had run out, and so had the enthusiasm of the participants (Heussenstamm, 1971).

Secondary Data Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data analysis . Secondary data does not result from firsthand research collected from primary sources, but are the already completed work of other researchers or data collected by an agency or organization. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines, or organizational data from any period in history.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or social media.

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization (WHO), publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of a recession. A racial demographic profile might be compared with data on education funding to examine the resources accessible by different groups.

One of the advantages of secondary data like old movies or WHO statistics is that it is nonreactive research (or unobtrusive research), meaning that it does not involve direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

Also, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not survey the topic from the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is public record. But these figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they’ve been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, when Robert S. Lynd and Helen Merrell Lynd gathered research in the 1920s, attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal insights about small U.S. communities. Today, it is an illustration of 1920s attitudes and values.

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In This Article Expand or collapse the "in this article" section Quantitative Methods in Sociological Research

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Quantitative Methods in Sociological Research by Erin Leahey LAST REVIEWED: 27 July 2011 LAST MODIFIED: 27 July 2011 DOI: 10.1093/obo/9780199756384-0044

Sociology develops, adopts, and adapts a wide variety of methods for understanding the social world. Realizing that this embarrassment of riches can bewilder the newcomer, this entry is intended to guide scholars through some of the main methods used by quantitative social scientists and some of the key resources for learning such methods. Because many sociologists in the United States receive foundational training in multivariate linear regression, this entry focuses on developments that go beyond this topic, including categorical data analysis, structural equation modeling, multilevel modeling, longitudinal data analysis, causal inference, and even network analysis. The recent wave of interest in mixed methods also merits inclusion. A section on critical reflections aims to encourage researchers to be reflective and thoughtful about the approach(es) they choose.

A number of professional associations are open to quantitative methodologists and researchers, including the two ASAs ( American Sociological Association and American Statistical Association ), the Population Association of American (PAA) , for demographers broadly defined, and the American Association for Public Opinion Research (AAPOR) for survey researchers and methodologists.

American Association of Public Opinion Research (AAPOR) .

Founded in 1947, AAPOR is an association of individuals who share an interest in survey research, qualitative and quantitative research methods, and public opinion data. Members come from academia, media, government, the nonprofit sector, and private industry. Meetings are held in even-numbered years.

American Sociological Association (ASA) .

The national professional association for sociologists, ASA serves as a reference for professional, ethical, and pedagogical topics; sponsors nine journals; and hosts an annual meeting.

American Statistical Association (ASA) .

ASA is the world’s largest community of statisticians and the second-oldest professional society in the United States. For 170 years, ASA has supported excellence in the development and dissemination of statistical science. Its members serve in industry, government, and academia, advancing research and promoting sound statistical practice to inform public policy and improve human welfare.

Population Association of America (PAA) .

PAA is a nonprofit organization that promotes research on population issues such as fertility, migration, health, and mortality. PAA sponsors the journal Demography .

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Research Methods: Quantitative and Qualitative Methods

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Book Review: Social Research Methods: Qualitative and Quantitative Approaches

William Lawrence Neuman, editor. Social Research Methods: Qualitative and Quantitative Approaches. 2014. Essex: Pearson Education Limited. 594 p. ISBN: 978-1-292-02023-5.

“The Art and Science of Asking Questions is the Source of All Knowledge”—Thomas Berger

In an endeavor to bridge the gap between knowledge and applicability, Neuman ( 2014 ) presents a meticulous and comprehensive amalgamation of concepts and theories, defining qualitative and quantitative research methods in his book “Social Research Methods: Qualitative and Quantitative Approaches.” A professor of sociology at the University of Wisconsin-Whitewater, William Lawrence Neuman has gained immense experience and has worked rigorously in his subject matter. He has authored seven books, numerous book chapters, and articles in the field of social sciences.

The book reviewed at present is the seventh edition of the “Social research methods: Qualitative and Quantitative Approaches,” which was published by Pearson Education Limited in 2014. The book was written to help aspiring researchers gain an in-depth understanding of research and its purpose while stressing the essentials and theoretical considerations of conducting research. With a total of 15 chapters, the book elucidates various research methods, balancing between qualitative and quantitative approaches with an aim to emphasize the conceptual framework, applications, strategies, and the pros and cons of each approach, along with highlighting the benefits of using a combination of the two approaches.

The current edition is divided into five parts—foundations of research; planning and preparation for research; quantitative research methods—collection and analysis of data; qualitative research—methods of collecting data and analysis; and lastly “communicating the results of research with others.”

Part one of the book consists of five chapters, shedding light on the basics to provide an understanding of the and how of research and its importance; types of research; theoretical conceptualizations; methodology; and conducting a literature review and ethics in research. For example, in chapter one, the author explains the need to learn how to conduct research, followed by explaining the use, scope, and target audience for research in chapter two. Furthermore, in chapter four, in the most beautifully structured manner, the author has elaborated on the philosophical foundations and paradigms of research.

Part two describes the basics of the process of conducting research. Divided into three chapters explaining qualitative and quantitative research in terms of—research designs and its various strategies; measurement of data; and sampling. Research design issues, reliability and validity, and the types of scales and inventories used are also discussed in this section, providing an integrative and inclusive view of the research process.

In the third part, the types of research and their processes are elaborated for collection of data and analysis in quantitative research. Spread across four chapters, the topics covered under this section include experimental research; survey research; non-reactive research and secondary analysis; and quantitative analysis of data. Whereas, the fourth part is dedicated to qualitative research. Described in two chapters, this section focuses on field and focus group research and analyzing qualitative data.

Parts three and four of the book do justice to the concepts by providing thorough information about the procedure and methods of research. It covers the history, advantages, disadvantages, uses, requirements, as well as gives details about the types of variables and statistical and non-statistical techniques that can be applied. Each chapter is enriched with figures, diagrams, and maps which aid in enhancing conceptual clarity. For example, chapter ten includes information about the latest technological advances such as online surveys and computer-assisted data collection and chapter 14 includes detailed figures depicting qualitative data analysis techniques, with a figure for each like narrative analysis.

The last part of the book and the final chapter provides detailed information on writing and publishing research reports as well as talks about the politics in social research. This part covers everything from why a research report is required, to understanding the writing process, formulating a research proposal, to discussing the ethics, limitations, advantages, and difficulties faced in conducting and publishing research.

In terms of the structure of the book, each chapter begins with the title and key pointers of the topics to be discussed, along with a quote or a small paragraph, which in a theoretical yet poetic style serves as a brief introduction to the topic. Needless to say, each topic mentioned is covered scrupulously and thoroughly in a holistic manner and is explained in-depth, clearly divided point-wise and under sub-categories. This helps in reducing the burden of information overload and aids in maintaining the readers interest.

The most noteworthy and distinguishing part of the book is the use of alternate means of representing and expressing information. Each topic is supported with various realistic examples, enriched with numerous figures, maps, diagrams, and is summarized in organized and structured tables for comparison and ease of understanding. The author has also included dialogue boxes in each chapter with short definitions of the topics in discussion. This is advantageous from a learning perspective as it provides a quick glimpse, simplifying the comprehension of concepts. It is these features that give the book an edge over other books of research.

The book also incorporates empirical evidence and statistical data in supporting its content and illustrations, making it more credible. The language used is simple and straightforward yet catchy in terms of grasping the reader's attention, making even complex theories and perspectives intelligible. At the end of each chapter, a list of key terms is provided, followed by a set of review questions. These questions are beneficial as a means of assessing conceptual clarity in addition to encouraging the reader to ruminate and indulge in lateral thinking over the subject matter.

Overall, the book is a valuable asset for the field of research. The confluence of theoretical concepts with realistic examples makes the book highly applicable and significant not just for students, but for anyone keen to venture into the realm of social research. Just like a building cannot withstand without a strong foundation, a researcher cannot exist without building and maintaining their repositories of knowledge. In conclusion, the book is a quintessential means of grasping and gaining mastery over research knowledge.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• Neuman W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches, 7th Edn . United Kingdom: Pearson Education Limited. [ Google Scholar ]

What is Qualitative in Research

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• Published: 28 October 2021
• Volume 44 , pages 599–608, ( 2021 )

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• Patrik Aspers 1 &
• Ugo Corte 2  

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

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What is Qualitative in Qualitative Research

What is “qualitative” in qualitative research why the answer does not matter but the question is important, unsettling definitions of qualitative research.

Avoid common mistakes on your manuscript.

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

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

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

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

Why a Definition?

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

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

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

We should not define qualitative research.

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

We can try to systematically define qualitative research.

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

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

What can a Definition Enable?

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

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

A Good Definition?

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

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

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

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

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

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

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

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

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

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

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

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Acknowledgements

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

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

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

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Principles of Sociological Inquiry: Qualitative and Quantitative Methods

Table of contents, licensing information, chapter 1: introduction.

• How Do We Know What We Know?
• Science, Social Science, and Sociology
• Why Should We Care?
• Design and Goals of This Text

Chapter 2: Linking Methods With Theory

• Micro, Meso, and Macro Approaches
• Paradigms, Theories, and How They Shape a Researcher’s Approach
• Inductive or Deductive? Two Different Approaches
• Revisiting an Earlier Question

Chapter 3: Research Ethics

• Research on Humans
• Specific Ethical Issues to Consider
• Ethics at Micro, Meso, and Macro Levels
• The Practice of Science Versus the Uses of Science

Chapter 4: Beginning a Research Project

• Starting Where You Already Are
• Is It Empirical?
• Is It Sociological?
• Is It a Question?

Chapter 5: Research Design

• Goals of the Research Project
• Qualitative or Quantitative? Some Specific Considerations
• Triangulation
• Components of a Research Project

Chapter 6: Defining and Measuring Concepts

• Measurement
• Conceptualization
• Operationalization
• Measurement Quality
• Complexities in Measurement

Chapter 7: Sampling

• Populations Versus Samples
• Sampling in Qualitative Research
• Sampling in Quantitative Research
• A Word of Caution: Questions to Ask About Samples

Chapter 8: Survey Research: A Quantitative Technique

• Chapter Introduction
• Survey Research: What Is It and When Should It Be Used?
• Pros and Cons of Survey Research
• Types of Surveys
• Designing Effective Questions and Questionnaires
• Analysis of Survey Data

Chapter 9: Interviews: Qualitative and Quantitative Approaches

• Interview Research: What Is It and When Should It Be Used?
• Qualitative Interview Techniques and Considerations
• Quantitative Interview Techniques and Considerations
• Issues to Consider for All Interview Types

Chapter 10: Field Research: A Qualitative Technique

• Field Research: What Is It and When to Use It?
• Pros and Cons of Field Research
• Field Notes
• Analysis of Field Research Data

Chapter 11: Unobtrusive Research: Qualitative and Quantitative Approaches

• Unobtrusive Research: What Is It and When to Use It?
• Pros and Cons of Unobtrusive Research
• Unobtrusive Data Collected by You
• Analyzing Others’ Data
• Reliability in Unobtrusive Research

Chapter 12: Other Methods of Data Collection and Analysis

• Focus Groups
• Experiments
• Ethnomethodology and Conversation Analysis

Chapter 13: Sharing Your Work

• Deciding What to Share and With Whom to Share It
• Presenting Your Research
• Writing Up Research Results
• Disseminating Findings

Chapter 14: Reading and Understanding Social Research

• Reading Reports of Sociological Research
• Being a Responsible Consumer of Research
• Media Reports of Sociological Research
• Sociological Research: It’s Everywhere

Chapter 15: Research Methods in the Real World

• Doing Research for a Living
• Doing Research for a Cause
• Public Sociology
• Revisiting an Earlier Question: Why Should We Care?

Social Sciences Research: Qualitative vs. Quantitative Research

• What is Social Science?

Qualitative vs. Quantitative Research

• Scholarly vs. Popular Sources This link opens in a new window
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Focus Groups

Social science research, or social research as it is sometimes called, stems from the natural sciences, and similar to its precursory field, it uses empirical, measurable outcomes to arrive at a conclusion. While natural scientists use the scientific method, social scientists often use quantitative research to go about their method of discovery.

Quantitative research "is the systematic examination of social phenomena, using statistical models and mathematical theories to develop, accumulate, and refine the scientific knowledge base" (" Quantitative Research," 2008 ). Quantitative research also provides "generalizable" findings, and according to Marlow (1993), is "characterized by hypothesis testing, using large samples, standardized measures, a deductive approach, and rigorously structured data collection instruments" (cited in "Quantitative Research").

As an alternative to quantitative research, qualitative research is also employed in social science research and is contrasted with quantitative research as such:

• Insider rather than outsider
• Person-centered rather than variable-centered
• Holistic rather than particularistic
• Depth rather than breadth

(" Qualitative Research, " 2008)

Trochim (2006), however, warns that researchers should not become so caught up in the polarizing differences between qualitative and quantiative research. He writes, "All quantitative data is based upon qualitative judgments; and all qualitative data can be described and manipulated numerically" (para. 3).

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Social Research Methods: Qualitative and Quantitative Approaches

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Qualitative Research

What is Qualitative Research?

Qualitative research is characterised by its aims, which relate to understanding some aspect of social life, and its methods which (in general) generate words, rather than numbers, as data for analysis.

For researchers more familiar with quantitative methods, which aim to measure something (such as the percentage of people with a particular disease in a community, or the number of households owning a bed net), the aims and methods of qualitative research can seem imprecise. Common criticisms include:

• samples are small and not necessarily representative of the broader population, so it is difficult to know how far we can generalise the results;
• the findings lack rigour;
• it is difficult to tell how far the findings are biased by the researcher’s own opinions.

Qualitative methods generally aim to understand the experiences and attitudes of patients, the community or health care worker. These methods aim to answer questions about the ‘what’, ‘how’ or ‘why’ of a phenomenon rather than ‘how many’ or ‘how much’, which are answered by quantitative methods.

Source: www.alnap.org/pool/files/ qualitative - research - method ology.pdf

Ethnography

The ethnographic approach to qualitative research comes largely from the field of anthropology. The emphasis in ethnography is on studying an entire culture. Originally, the idea of a culture was tied to the notion of ethnicity and geographic location (e.g., the culture of the Trobriand Islands), but it has been broadened to include virtually any group or organization. That is, we can study the "culture" of a business or defined group (e.g., a Rotary club).

Source: http://www.socialresearchmethods.net/kb/qualapp.php

Methods of Qualitative Research

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

Direct observation: With direct observation, a researcher studies people as they go about their daily lives without participating or interfering.

In-depth interviews: Researchers conduct in-depth interviews by speaking with participants in a one-on-one setting. Sometimes a researcher approaches the interview with a predetermined list of questions or topics for discussion but allows the conversation to evolve based on how the participant responds.

Ethnographic observation: Ethnographic observation is the most intensive and in-depth observational method.

Open-ended surveys: While many surveys are designed to generate quantitative data, many are also designed with open-ended questions that allow for the generation and analysis of qualitative data.

Oral History

UCLA Library Center for Oral History Research - https://oralhistory.library.ucla.edu/

The UCLA Center for Oral History Research (COHR) conducts in-depth, multi-session oral history interviews with individuals who have been a part of the history of Los Angeles and its many communities. COHR has particularly strong collections in the history of social movements, communities of color, the arts, Los Angeles politics and government, and the history of UCLA.

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Quantitative and Qualitative Methods in sociology

Quantitative and qualitative methods are two primary research approaches used in sociology. The purpose of quantitative research is to understand patterns, correlations, and causality in social phenomena by gathering and analysing numerical data using statistical methods. Statistical analysis of large datasets and surveys are some examples of quantitative methods used in sociology.

The goal of qualitative research, however, is to gain an understanding of social phenomena through the collection and analysis of nonnumerical data, such as interview transcripts, observations, and texts. Examples of qualitative methods used in sociology include ethnography, content analysis, and grounded theory. In order to choose a research method, it is important to consider the research question, the type of data required, and the approach to the research. Some researchers also use mixed methods to get the data according to the requirements.

Primary research methods

Quantitative methods.

Surveys − This method involves the use of questionnaires to gather data from a large group of people. The surveys can be administered face-to-face, through email or online platforms.

Experiments − Researchers manipulate the independent variable in controlled laboratory settings to observe the effect on the dependent variable.

Observational Studies − This method involves observing and recording the behaviour of people in their natural environment. The researcher can use either structured or unstructured observations.

Content Analysis − This method involves the analysis of text, audio, or visual media to identify themes, patterns, or trends in the data.

Qualitative Methods

Interviews − This method involves face-to-face conversations between the researcher and participants to gather data on their experiences, perceptions, and attitudes.

Focus Groups − This method involves a group discussion with a moderator to gather data on a specific topic or issue.

Ethnography − This method involves the immersion of the researcher in the culture or group being studied to understand their experiences, values, and beliefs.

Case Studies − This method involves an in-depth analysis of a single individual, group, or event to understand their experiences, motivations, and behaviours.

Ethnography

Ethnography is a qualitative research method used to understand social phenomena within a particular culture or community. The method involves the observation of people's behaviour in their natural environment and the recording of their experiences. Clifford Geertz is known for his contribution to the development of ethnography as a research method in sociology. In his book, "The Interpretation of Cultures," Geertz emphasised the importance of understanding the symbolic meaning of social behaviour within a cultural context.

Survey Method

Data is collected from a large number of people using surveys in quantitative research methods. A questionnaire or interview is used to gather information about people's attitudes, beliefs, and behaviours. Surveys are often used in sociology to measure social phenomena such as social inequality, prejudice, and discrimination. Surveys can provide a representative sample of the population being studied and can be used to generalise findings to the larger population.

Historical Method

The historical method is a qualitative research method used to understand social phenomena within a historical context. The method involves the analysis of historical documents and other artefacts to reconstruct the social, political, and economic conditions of a particular time period. The historical method is often used in sociology to study social movements, political revolutions, and other significant events that have shaped society.

Comparative Method

The comparative method is a quantitative research method used to compare social phenomena across different societies or cultures. The method involves the collection of data from multiple sources and the analysis of similarities and differences between the data. Herbert Spencer and Emile Durkheim are known for their contributions to the development of the comparative method in sociology. Spencer believed that social phenomena could be explained by the evolution of societies, while Durkheim emphasised the importance of studying social facts and their relationships to one another.

In conclusion, quantitative and qualitative research methods are both essential to the study of social phenomena in sociology. Ethnography, surveys, the historical method, and the comparative method are just a few examples of research methods used in sociology. The method that is the most appropriate for the researchers' research question will vary based on the strengths and limitations of each method. By combining quantitative and qualitative methods, sociologists can develop theory and practice and gain a comprehensive understanding of social phenomena.

Q1. How do qualitative and quantitative research methods differ in sociology?

Ans. A qualitative research method focuses on interpreting human behaviour and interpreting non-numerical data, whereas a quantitative method focuses on collecting numerical data.

Q2. What are some of the limitations of the survey method in sociology?

Ans. The survey method may suffer from response bias, where respondents may provide socially desirable responses or may not be truthful. Additionally, the survey method may not be suitable for exploring complex social phenomena that require in-depth analysis.

Q3. What are the methods researchers can use to ensure that their research findings are valid and reliable?

Ans. Researchers can ensure the validity and reliability of their research findings by using a systematic and rigorous research design, selecting appropriate data collection methods, and analysing the data using reliable statistical and qualitative analysis techniques. Additionally, researchers should be transparent in their reporting and acknowledge the limitations of their research

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

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What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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Qualitative vs. Quantitative: Key Differences in Research Types

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Let's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data.

With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics.

Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them.

What Is Qualitative Research?

What is quantitative research, qualitative vs. quantitative research: 3 key differences, benefits of combining qualitative and quantitative research.

Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis.

In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions.

This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved.

Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data.

Examples of How to Use Qualitative Research

The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research.

Here are just a few ways you can use qualitative research.

• To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values.
• To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments.
• To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns.

Types of Qualitative Data Collection

Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including:

• Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes.
• Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions.
• Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic.
• Visual data : You can learn new information through images, photographs, videos or other visual materials.

Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis.

Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions.

Examples of How to Use Quantitative Research

Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods.

• Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period.
• Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood.

Types of Quantitative Data

Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use.

• Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina.
• Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins.
• Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry.

Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ.

• Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis.
• Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data.
• Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences.

You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including:

• A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability.
• Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results.
• Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study.

This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor.

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7.3: Sampling in Quantitative Research

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Learning Objectives

• Describe how probability sampling differs from nonprobability sampling.
• Define generalizability, and describe how it is achieved in probability samples.
• Identify the various types of probability samples, and provide a brief description of each.

Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory or evaluation research), quantitative researchers tend to rely on probability sampling techniques. The goals and techniques associated with probability samples differ from those of nonprobability samples. We’ll explore those unique goals and techniques in this section.

Probability Sampling

Unlike nonprobability sampling, probability sampling refers to sampling techniques for which a person’s (or event’s) likelihood of being selected for membership in the sample is known. You might ask yourself why we should care about a study element’s likelihood of being selected for membership in a researcher’s sample. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample from which to collect data. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted. If, for example, you wish to be able to say something about differences between men and women at the end of your study, you better make sure that your sample doesn’t contain only women. That’s a bit of an oversimplification, but the point with representativeness is that if your population varies in some way that is important to your study, your sample should contain the same sorts of variation.

Obtaining a representative sample is important in probability sampling because a key goal of studies that rely on probability samples is generalizability . In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. In order to achieve generalizability, a core principle of probability sampling is that all elements in the researcher’s target population have an equal chance of being selected for inclusion in the study. In research, this is the principle of random selection . Random selection is a mathematical process that we won’t go into too much depth about here, but if you have taken or plan to take a statistics course, you’ll learn more about it there. The important thing to remember about random selection here is that, as previously noted, it is a core principal of probability sampling. If a researcher uses random selection techniques to draw a sample, he or she will be able to estimate how closely the sample represents the larger population from which it was drawn by estimating the sampling error. Sampling error is a statistical calculation of the difference between results from a sample and the actual parameters of a population.

Types of Probability Samples

There are a variety of probability samples that researchers may use. These include simple random samples, systematic samples, stratified samples, and cluster samples.

Simple random samples are the most basic type of probability sample, but their use is not particularly common. Part of the reason for this may be the work involved in generating a simple random sample. To draw a simple random sample, a researcher starts with a list of every single member, or element, of his or her population of interest. This list is sometimes referred to as a sampling frame . Once that list has been created, the researcher numbers each element sequentially and then randomly selects the elements from which he or she will collect data. To randomly select elements, researchers use a table of numbers that have been generated randomly. There are several possible sources for obtaining a random number table. Some statistics and research methods textbooks offer such tables as appendices to the text. Perhaps a more accessible source is one of the many free random number generators available on the Internet. A good online source is the website Stat Trek, which contains a random number generator that you can use to create a random number table of whatever size you might need ( stattrek.com/Tables/Random.aspx ). Randomizer.org also offers a useful random number generator ( http://randomizer.org ).

As you might have guessed, drawing a simple random sample can be quite tedious. Systematic sampling techniques are somewhat less tedious but offer the benefits of a random sample. As with simple random samples, you must be able to produce a list of every one of your population elements. Once you’ve done that, to draw a systematic sample you’d simply select every k th element on your list. But what is k , and where on the list of population elements does one begin the selection process? k is your selection interval or the distance between the elements you select for inclusion in your study. To begin the selection process, you’ll need to figure out how many elements you wish to include in your sample. Let’s say you want to interview 25 fraternity members on your campus, and there are 100 men on campus who are members of fraternities. In this case, your selection interval, or k , is 4. To arrive at 4, simply divide the total number of population elements by your desired sample size. This process is represented in Figure 7.5.

Figure 7.5 Formula for Determining Selection Interval for Systematic Sample

To determine where on your list of population elements to begin selecting the names of the 25 men you will interview, select a random number between 1 and k , and begin there. If we randomly select 3 as our starting point, we’d begin by selecting the third fraternity member on the list and then select every fourth member from there. This might be easier to understand if you can see it visually. Table 7.2 lists the names of our hypothetical 100 fraternity members on campus. You’ll see that the third name on the list has been selected for inclusion in our hypothetical study, as has every fourth name after that. A total of 25 names have been selected.

There is one clear instance in which systematic sampling should not be employed. If your sampling frame has any pattern to it, you could inadvertently introduce bias into your sample by using a systemic sampling strategy. This is sometimes referred to as the problem of periodicity . Periodicity refers to the tendency for a pattern to occur at regular intervals. Let’s say, for example, that you wanted to observe how people use the outdoor public spaces on your campus. Perhaps you need to have your observations completed within 28 days and you wish to conduct four observations on randomly chosen days. Table 7.3 shows a list of the population elements for this example. To determine which days we’ll conduct our observations, we’ll need to determine our selection interval. As you’ll recall from the preceding paragraphs, to do so we must divide our population size, in this case 28 days, by our desired sample size, in this case 4 days. This formula leads us to a selection interval of 7. If we randomly select 2 as our starting point and select every seventh day after that, we’ll wind up with a total of 4 days on which to conduct our observations. You’ll see how that works out in the following table.

Do you notice any problems with our selection of observation days? Apparently we’ll only be observing on Tuesdays. As you have probably figured out, that isn’t such a good plan if we really wish to understand how public spaces on campus are used. My guess is that weekend use probably differs from weekday use, and that use may even vary during the week, just as class schedules do. In cases such as this, where the sampling frame is cyclical, it would be better to use a stratified sampling technique . In stratified sampling, a researcher will divide the study population into relevant subgroups and then draw a sample from each subgroup. In this example, we might wish to first divide our sampling frame into two lists: weekend days and weekdays. Once we have our two lists, we can then apply either simple random or systematic sampling techniques to each subgroup.

Stratified sampling is a good technique to use when, as in our example, a subgroup of interest makes up a relatively small proportion of the overall sample. In our example of a study of use of public space on campus, we want to be sure to include weekdays and weekends in our sample, but because weekends make up less than a third of an entire week, there’s a chance that a simple random or systematic strategy would not yield sufficient weekend observation days. As you might imagine, stratified sampling is even more useful in cases where a subgroup makes up an even smaller proportion of the study population, say, for example, if we want to be sure to include both men’s and women’s perspectives in a study, but men make up only a small percentage of the population. There’s a chance simple random or systematic sampling strategy might not yield any male participants, but by using stratified sampling, we could ensure that our sample contained the proportion of men that is reflective of the larger population.

Up to this point in our discussion of probability samples, we’ve assumed that researchers will be able to access a list of population elements in order to create a sampling frame. This, as you might imagine, is not always the case. Let’s say, for example, that you wish to conduct a study of hairstyle preferences across the United States. Just imagine trying to create a list of every single person with (and without) hair in the country. Basically, we’re talking about a list of every person in the country. Even if you could find a way to generate such a list, attempting to do so might not be the most practical use of your time or resources. When this is the case, researchers turn to cluster sampling. Cluster sampling occurs when a researcher begins by sampling groups (or clusters) of population elements and then selects elements from within those groups.

Let’s take a look at a couple more examples. Perhaps you are interested in the workplace experiences of public librarians. Chances are good that obtaining a list of all librarians that work for public libraries would be rather difficult. But I’ll bet you could come up with a list of all public libraries without too much hassle. Thus you could draw a random sample of libraries (your cluster) and then draw another random sample of elements (in this case, librarians) from within the libraries you initially selected. Cluster sampling works in stages. In this example, we sampled in two stages. As you might have guessed, sampling in multiple stages does introduce the possibility of greater error (each stage is subject to its own sampling error), but it is nevertheless a highly efficient method.

Jessica Holt and Wayne Gillespie (2008)Holt, J. L., & Gillespie, W. (2008). Intergenerational transmission of violence, threatened egoism, and reciprocity: A test of multiple pychosocial factors affecting intimate partner violence. American Journal of Criminal Justice, 33 , 252–266. used cluster sampling in their study of students’ experiences with violence in intimate relationships. Specifically, the researchers randomly selected 14 classes on their campus and then drew a random subsample of students from those classes. But you probably know from your experience with college classes that not all classes are the same size. So if Holt and Gillespie had simply randomly selected 14 classes and then selected the same number of students from each class to complete their survey, then students in the smaller of those classes would have had a greater chance of being selected for the study than students in the larger classes. Keep in mind with random sampling the goal is to make sure that each element has the same chance of being selected. When clusters are of different sizes, as in the example of sampling college classes, researchers often use a method called probability proportionate to size (PPS). This means that they take into account that their clusters are of different sizes. They do this by giving clusters different chances of being selected based on their size so that each element within those clusters winds up having an equal chance of being selected.

KEY TAKEAWAYS

• In probability sampling, the aim is to identify a sample that resembles the population from which it was drawn.
• There are several types of probability samples including simple random samples, systematic samples, stratified samples, and cluster samples.
• Imagine that you are about to conduct a study of people’s use of public parks. Explain how you could employ each of the probability sampling techniques described earlier to recruit a sample for your study.
• Of the four probability sample types described, which seems strongest to you? Which seems weakest? Explain.

Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

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What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

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

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

At Fullstory, we understand the importance of data, which is why we created a behavioral data platform that analyzes customer data for better insights. Our platform delivers a complete, retroactive view of how people interact with your site or app—and analyzes every point of user interaction so you can scale.

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

Fullstory's DXI platform combines the quantitative insights of product analytics with picture-perfect session replay for complete context that helps you answer questions, understand issues, and uncover customer opportunities.

Start a free 14-day trial to see how Fullstory can help you combine your most invaluable quantitative and qualitative insights and eliminate blind spots.

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Quantitative or Qualitative?

Hi, I'm a freshman student from Indonesia. On the 2nd semester of our freshman year, we started to learn more about social research methods, that it Quali and Quanti. So far, we already doing field work collecting data, both Quali and Quanti. And i find Quantitative methods is interesting (ironically, i choose sociology bcs i thought there's no math in the study), from collecting data, how to use the software, and started to interpret the data using stata, which for me a lot of work but still fun. In other hand, Qualitative is such a hard work. From the start you need to review a bunch of literatures, you need to write down everything from your interviews, writing the verbatim (it tooks me 12 hours to verbatim a 30 minutes interview), coding and blah blah blah.

Now, Quantitative seems interesting and i expect i'm gonna pursue that in my next studies. But will it get harder or getting boring as Qualitative? or maybe further down the road i'll find Qualitative is more fun?

For all of those who already experience this, what do you think? Thanks in advance!