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

Introduction, professional associations.

Data Sources
Data Archives
Statistical Software Packages
Research Design
Survey Research
Categorical Data Analysis
Longitudinal Data Analysis
Structural Equation Modeling
Multilevel Modeling
Causal Inference
Critical Reflections
Mixed Methods
Network Analysis
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Quantitative Methods in Sociological Research by Erin Leahey LAST REVIEWED: 13 November 2018 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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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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Knowledge Base

Methodology

What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

In descriptive research , you simply seek an overall summary of your study variables.
In correlational research , you investigate relationships between your study variables.
In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

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

Research bias

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

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

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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A Quick Guide to Quantitative Research in the Social Sciences

(11 reviews)

Christine Davies, Carmarthen, Wales

Last Update: 2021

Publisher: University of Wales Trinity Saint David

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 3/9/24

Comprehensiveness rating: 3 see less

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers references to other resources that can be used to deepen the knowledge. The text does not include a glossary or index. The references in the figures for each chapter are not included in the reference section. It would be helpful to include those.

Content Accuracy rating: 4

Overall, the text is accurate. For example, Figure 1 on page 6 provides a clear overview of the research process. It includes general definitions of primary and secondary research. It would be helpful to include more details to explain some of the examples before they are presented. For instance, the example on page 5 was unclear how it pertains to the literature review section.

Relevance/Longevity rating: 4

In general, the text is relevant and up-to-date. The text includes many inferences of moving from qualitative to quantitative analysis. This was surprising to me as a quantitative researcher. The author mentions that moving from a qualitative to quantitative approach should only be done when needed. As a predominantly quantitative researcher, I would not advice those interested in transitioning to using a qualitative approach that qualitative research would enhance their research—not something that should only be done if you have to.

Clarity rating: 4

The text is written in a clear manner. It would be helpful to the reader if there was a description of the tables and figures in the text before they are presented.

Consistency rating: 4

The framework for each chapter and terminology used are consistent.

Modularity rating: 4

The text is clearly divided into sections within each chapter. Overall, the chapters are a similar brief length except for the chapter on data analysis, which is much more comprehensive than others.

Organization/Structure/Flow rating: 4

The topics in the text are presented in a clear and logical order. The order of the text follows the conventional research methodology in social sciences.

Interface rating: 5

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader.

Grammatical Errors rating: 3

There are some grammatical/typographical errors throughout. Of note, for Section 5 in the table of contents. “The” should be capitalized to start the title. In the title for Table 3, the “t” in typical should be capitalized.

Cultural Relevance rating: 4

The examples are culturally relevant. The text is geared towards learners in the UK, but examples are relevant for use in other countries (i.e., United States). I did not see any examples that may be considered culturally insensitive or offensive in any way.

I teach a course on research methods in a Bachelor of Science in Public Health program. I would consider using some of the text, particularly in the analysis chapter to supplement the current textbook in the future.

Reviewed by Finn Bell, Assistant Professor, University of Michigan, Dearborn on 1/3/24

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary. read more

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary.

Content Accuracy rating: 5

As far as I can tell, the text is accurate, error-free and unbiased.

Relevance/Longevity rating: 5

This text is up-to-date, and given the content, unlikely to become obsolete any time soon.

Clarity rating: 5

The text is very clear and accessible.

Consistency rating: 5

The text is internally consistent.

Modularity rating: 5

Given how short the text is, it seems unnecessary to divide it into smaller readings, nonetheless, it is clearly labelled such that an instructor could do so.

Organization/Structure/Flow rating: 5

The text is well-organized and brings readers through basic quantitative methods in a logical, clear fashion.

Easy to navigate. Only one table that is split between pages, but not in a way that is confusing.

Grammatical Errors rating: 5

There were no noticeable grammatical errors.

The examples in this book don't give enough information to rate this effectively.

This text is truly a very quick guide at only 26 double-spaced pages. Nonetheless, Davies packs a lot of information on the basics of quantitative research methods into this text, in an engaging way with many examples of the concepts presented. This guide is more of a brief how-to that takes readers as far as how to select statistical tests. While it would be impossible to fully learn quantitative research from such a short text, of course, this resource provides a great introduction, overview, and refresher for program evaluation courses.

Reviewed by Shari Fedorowicz, Adjunct Professor, Bridgewater State University on 12/16/22

Comprehensiveness rating: 5 see less

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing the reader with the ability to distinguish two terms that frequently get confused. In addition, links and outside resources are provided to deepen the understanding as an option for the reader. The use of these links, coupled with diagrams and examples make this text comprehensive.

The content is mostly accurate. Given that it is a quick guide, the author chose a good selection of which types of research designs to include. However, some are not provided. For example, correlational or cross-correlational research is omitted and is not discussed in Section 3, but is used as a statistical example in the last section.

Examples utilized were appropriate and associated with terms adding value to the learning. The tables that included differentiation between types of statistical tests along with a parametric/nonparametric table were useful and relevant.

The purpose to the text and how to use this guide book is stated clearly and is established up front. The author is also very clear regarding the skill level of the user. Adding to the clarity are the tables with terms, definitions, and examples to help the reader unpack the concepts. The content related to the terms was succinct, direct, and clear. Many times examples or figures were used to supplement the narrative.

The text is consistent throughout from contents to references. Within each section of the text, the introductory paragraph under each section provides a clear understanding regarding what will be discussed in each section. The layout is consistent for each section and easy to follow.

The contents are visible and address each section of the text. A total of seven sections, including a reference section, is in the contents. Each section is outlined by what will be discussed in the contents. In addition, within each section, a heading is provided to direct the reader to the subtopic under each section.

The text is well-organized and segues appropriately. I would have liked to have seen an introductory section giving a narrative overview of what is in each section. This would provide the reader with the ability to get a preliminary glimpse into each upcoming sections and topics that are covered.

The book was easy to navigate and well-organized. Examples are presented in one color, links in another and last, figures and tables. The visuals supplemented the reading and placed appropriately. This provides an opportunity for the reader to unpack the reading by use of visuals and examples.

No significant grammatical errors.

Cultural Relevance rating: 5

The text is not offensive or culturally insensitive. Examples were inclusive of various races, ethnicities, and backgrounds.

This quick guide is a beneficial text to assist in unpacking the learning related to quantitative statistics. I would use this book to complement my instruction and lessons, or use this book as a main text with supplemental statistical problems and formulas. References to statistical programs were appropriate and were useful. The text did exactly what was stated up front in that it is a direct guide to quantitative statistics. It is well-written and to the point with content areas easy to locate by topic.

Reviewed by Sarah Capello, Assistant Professor, Radford University on 1/18/22

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text. read more

Comprehensiveness rating: 4 see less

The content is mostly accurate. I would have preferred a few nuances to be hashed out a bit further to avoid potential reader confusion or misunderstanding of the concepts presented.

The content is current; however, some of the references cited in the text are outdated. Newer editions of those texts exist.

The text is very accessible and readable for a variety of audiences. Key terms are well-defined.

There are no content discrepancies within the text. The author even uses similarly shaped graphics for recurring purposes throughout the text (e.g., arrow call outs for further reading, rectangle call outs for examples).

The content is chunked nicely by topics and sections. If it were used for a course, it would be easy to assign different sections of the text for homework, etc. without confusing the reader if the instructor chose to present the content in a different order.

The author follows the structure of the research process. The organization of the text is easy to follow and comprehend.

All of the supplementary images (e.g., tables and figures) were beneficial to the reader and enhanced the text.

There are no significant grammatical errors.

I did not find any culturally offensive or insensitive references in the text.

This text does the difficult job of introducing the complicated concepts and processes of quantitative research in a quick and easy reference guide fairly well. I would not depend solely on this text to teach students about quantitative research, but it could be a good jumping off point for those who have no prior knowledge on this subject or those who need a gentle introduction before diving in to more advanced and complex readings of quantitative research methods.

Reviewed by J. Marlie Henry, Adjunct Faculty, University of Saint Francis on 12/9/21

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of thought. There is no glossary but, for a guide of this length, a glossary does not seem like it would enhance the guide significantly.

The content is relatively accurate. Expanding the content a bit more or explaining that the methods and designs presented are not entirely inclusive would help. As there are different schools of thought regarding what should/should not be included in terms of these designs and methods, simply bringing attention to that and explaining a bit more would help.

Relevance/Longevity rating: 3

This content needs to be updated. Most of the sources cited are seven or more years old. Even more, it would be helpful to see more currently relevant examples. Some of the source authors such as Andy Field provide very interesting and dynamic instruction in general, but they have much more current information available.

The language used is clear and appropriate. Unnecessary jargon is not used. The intent is clear- to communicate simply in a straightforward manner.

The guide seems to be internally consistent in terms of terminology and framework. There do not seem to be issues in this area. Terminology is internally consistent.

For a guide of this length, the author structured this logically into sections. This guide could be adopted in whole or by section with limited modifications. Courses with fewer than seven modules could also logically group some of the sections.

This guide does present with logical organization. The topics presented are conceptually sequenced in a manner that helps learners build logically on prior conceptualization. This also provides a simple conceptual framework for instructors to guide learners through the process.

Interface rating: 4

The visuals themselves are simple, but they are clear and understandable without distracting the learner. The purpose is clear- that of learning rather than visuals for the sake of visuals. Likewise, navigation is clear and without issues beyond a broken link (the last source noted in the references).

This guide seems to be free of grammatical errors.

It would be interesting to see more cultural integration in a guide of this nature, but the guide is not culturally insensitive or offensive in any way. The language used seems to be consistent with APA's guidelines for unbiased language.

Reviewed by Heng Yu-Ku, Professor, University of Northern Colorado on 5/13/21

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive... read more

For the most part, the content is accurate and unbiased. However, the author only includes four types of research designs used on the social sciences that contain quantitative elements: 1. Mixed method, 2) Case study, 3) Quasi-experiment, and 3) Action research. I wonder why the correlational research is not included as another type of quantitative research design as it has been introduced and emphasized in section 6 by the author.

I believe the content is up-to-date and that necessary updates will be relatively easy and straightforward to implement.

The text is easy to read and provides adequate context for any technical terminology used. However, the author could provide more detailed information about estimating the minimum sample size but not just refer the readers to use the online sample calculators at a different website.

The text is internally consistent in terms of terminology and framework. The author provides the right amount of information with additional information or resources for the readers.

The text includes seven sections. Therefore, it is easier for the instructor to allocate or divide the content into different weeks of instruction within the course.

Yes, the topics in the text are presented in a logical and clear fashion. The author provides clear and precise terminologies, summarizes important content in Table or Figure forms, and offers examples in each section for readers to check their understanding.

The interface of the book is consistent and clear, and all the images and charts provided in the book are appropriate. However, I did encounter some navigation problems as a couple of links are not working or requires permission to access those (pages 10 and 27).

No grammatical errors were found.

No culturally incentive or offensive in its language and the examples provided were found.

As the book title stated, this book provides “A Quick Guide to Quantitative Research in Social Science. It offers easy-to-read information and introduces the readers to the research process, such as research questions, research paradigms, research process, research designs, research methods, data collection, data analysis, and data discussion. However, some links are not working or need permissions to access them (pages 10 and 27).

Reviewed by Hsiao-Chin Kuo, Assistant Professor, Northeastern Illinois University on 4/26/21, updated 4/28/21

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and process, discusses methods, data collection and analysis, and ends with writing a research report. It also identifies its target readers/users as those begins to explore quantitative research. It would be helpful to include more examples for readers/users who are new to quantitative research.

Its content is mostly accurate and no bias given its nature as a quick guide. Yet, it is also quite simplified, such as its explanations of mixed methods, case study, quasi-experimental research, and action research. It provides resources for extended reading, yet more recent works will be helpful.

The book is relevant given its nature as a quick guide. It would be helpful to provide more recent works in its resources for extended reading, such as the section for Survey Research (p. 12). It would also be helpful to include more information to introduce common tools and software for statistical analysis.

The book is written with clear and understandable language. Important terms and concepts are presented with plain explanations and examples. Figures and tables are also presented to support its clarity. For example, Table 4 (p. 20) gives an easy-to-follow overview of different statistical tests.

The framework is very consistent with key points, further explanations, examples, and resources for extended reading. The sample studies are presented following the layout of the content, such as research questions, design and methods, and analysis. These examples help reinforce readers' understanding of these common research elements.

The book is divided into seven chapters. Each chapter clearly discusses an aspect of quantitative research. It can be easily divided into modules for a class or for a theme in a research method class. Chapters are short and provides additional resources for extended reading.

The topics in the chapters are presented in a logical and clear structure. It is easy to follow to a degree. Though, it would be also helpful to include the chapter number and title in the header next to its page number.

The text is easy to navigate. Most of the figures and tables are displayed clearly. Yet, there are several sections with empty space that is a bit confusing in the beginning. Again, it can be helpful to include the chapter number/title next to its page number.

Grammatical Errors rating: 4

No major grammatical errors were found.

There are no cultural insensitivities noted.

Given the nature and purpose of this book, as a quick guide, it provides readers a quick reference for important concepts and terms related to quantitative research. Because this book is quite short (27 pages), it can be used as an overview/preview about quantitative research. Teacher's facilitation/input and extended readings will be needed for a deeper learning and discussion about aspects of quantitative research.

Reviewed by Yang Cheng, Assistant Professor, North Carolina State University on 1/6/21

It covers the most important topics such as research progress, resources, measurement, and analysis of the data. read more

It covers the most important topics such as research progress, resources, measurement, and analysis of the data.

The book accurately describes the types of research methods such as mixed-method, quasi-experiment, and case study. It talks about the research proposal and key differences between statistical analyses as well.

The book pinpointed the significance of running a quantitative research method and its relevance to the field of social science.

The book clearly tells us the differences between types of quantitative methods and the steps of running quantitative research for students.

The book is consistent in terms of terminologies such as research methods or types of statistical analysis.

It addresses the headlines and subheadlines very well and each subheading should be necessary for readers.

The book was organized very well to illustrate the topic of quantitative methods in the field of social science.

The pictures within the book could be further developed to describe the key concepts vividly.

The textbook contains no grammatical errors.

It is not culturally offensive in any way.

Overall, this is a simple and quick guide for this important topic. It should be valuable for undergraduate students who would like to learn more about research methods.

Reviewed by Pierre Lu, Associate Professor, University of Texas Rio Grande Valley on 11/20/20

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas. read more

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas.

Mostly accurate content.

As a quick guide, content is highly relevant.

Succinct and clear.

Internally, the text is consistent in terms of terminology used.

The text is easily and readily divisible into smaller sections that can be used as assignments.

I like that there are examples throughout the book.

Easy to read. No interface/ navigation problems.

No grammatical errors detected.

I am not aware of the culturally insensitive description. After all, this is a methodology book.

I think the book has potential to be adopted as a foundation for quantitative research courses, or as a review in the first weeks in advanced quantitative course.

Reviewed by Sarah Fischer, Assistant Professor, Marymount University on 7/31/20

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable). read more

Content Accuracy rating: 1

Contains VERY significant errors, such as saying that one can "accept" a hypothesis. (One of the key aspect of hypothesis testing is that one either rejects or fails to reject a hypothesis, but NEVER accepts a hypothesis.)

Very relevant to those experiencing the research process for the first time. However, it is written by someone working in the natural sciences but is a text for social sciences. This does not explain the errors, but does explain why sometimes the author assumes things about the readers ("hail from more subjectivist territory") that are likely not true.

Clarity rating: 3

Some statistical terminology not explained clearly (or accurately), although the author has made attempts to do both.

Very consistently laid out.

Chapters are very short yet also point readers to outside texts for additional information. Easy to follow.

Generally logically organized.

Easy to navigate, images clear. The additional sources included need to linked to.

Minor grammatical and usage errors throughout the text.

Makes efforts to be inclusive.

The idea of this book is strong--short guides like this are needed. However, this book would likely be strengthened by a revision to reduce inaccuracies and improve the definitions and technical explanations of statistical concepts. Since the book is specifically aimed at the social sciences, it would also improve the text to have more examples that are based in the social sciences (rather than the health sciences or the arts).

Reviewed by Michelle Page, Assistant Professor, Worcester State University on 5/30/20

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new researcher would not be able to use this as a stand alone guide for quantitative pursuits without having a supplemental text that explains the steps in the process more comprehensively. The introduction does provide this caveat.

Content Accuracy rating: 3

There are no biases or errors that could be distinguished; however, it’s simplicity in content, although accurate for an outline of process, may lack a conveyance of the deeper meanings behind the specific processes explained about qualitative research.

The content is outlined in traditional format to highlight quantitative considerations for formatting research foundational pieces. The resources/references used to point the reader to literature sources can be easily updated with future editions.

The jargon in the text is simple to follow and provides adequate context for its purpose. It is simplified for its intention as a guide which is appropriate.

Each section of the text follows a consistent flow. Explanation of the research content or concept is defined and then a connection to literature is provided to expand the readers understanding of the section’s content. Terminology is consistent with the qualitative process.

As an “outline” and guide, this text can be used to quickly identify the critical parts of the quantitative process. Although each section does not provide deeper content for meaningful use as a stand alone text, it’s utility would be excellent as a reference for a course and can be used as an content guide for specific research courses.

The text’s outline and content are aligned and are in a logical flow in terms of the research considerations for quantitative research.

The only issue that the format was not able to provide was linkable articles. These would have to be cut and pasted into a browser. Functional clickable links in a text are very successful at leading the reader to the supplemental material.

No grammatical errors were noted.

This is a very good outline “guide” to help a new or student researcher to demystify the quantitative process. A successful outline of any process helps to guide work in a logical and systematic way. I think this simple guide is a great adjunct to more substantial research context.

Section 1: What will this resource do for you?
Section 2: Why are you thinking about numbers? A discussion of the research question and paradigms.
Section 3: An overview of the Research Process and Research Designs
Section 4: Quantitative Research Methods
Section 5: the data obtained from quantitative research
Section 6: Analysis of data
Section 7: Discussing your Results

Ancillary Material

About the book.

This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for maths, then this booklet should be a real help.

The booklet was amended in 2022 to take into account previous review comments.

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Alexandra Killewald

Professor of sociology.

Soc 156: Quantitative Methods in Sociology

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Quantitative Methods

Jeremy Freese

David Grusky

Michelle Jackson

Michael Rosenfeld

Jan G. Voelkel

Sociological Research Methods: Qualitative and Quantitative Methods

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

Research is carefully observing patterns for searching for new facts or terms in any kind of subject. For example, there are several research centers for obtaining new results for better performance, say Bhabha Atomic Research center which specializes in nuclear fission and fusion reactions.

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.

Statistical data

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.

Comparative Method

It can be easily guessed from the name itself that the method includes comparing different values. For example, in the science laboratory, there are comparators which compare different values of resistance and thus a mean value is written. Same is the case with sociology, different societies are compared by sociologist and after observing each and every factor they develop some theories under their research work. Marx, Durkheim , and Weber are said to be the inventor of this method which profoundly deals with the logic. The three of them compared many societies with each other to give some of the wonderful research work. Marx studied the phenomenon of difference and thus agreed that societies transform via many changes.

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.

Field Methods

Science experiments are generally done in respective laboratories. But sociology experiments are performed in a natural arrangement outside the labs. For example, sociologists can carry out experiments in which they can observe people interaction ability thus categorizing them into introverts, ambivert, and extroverts. The advantage of this method is that it allows the expansion of areas where the experiment can be performed and better results are obtained as compared to other methods. But likewise, its biggest disadvantage is the variance can cause experiment results to differ unlike experiments performed in a science laboratory. This error is also termed as a Hawthorne effect. The experiments do not account for generalizing any theory as a particular amount of people can be tested.

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.

Participant Observation

It can be seen as a modification of Field methods as this method involves the researcher too. The researcher has to keep a mindset as an observant which will decrease the chances of a biased opinion as the perception will not be compressed. The field researchers, data or any theory is studied comprehensively as a researcher and participant point of view.

Direct Observation

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.

Unstructured Interviewing
These interviews are completely in contrast to conventionally structured interviews. They differ in various aspects. In unstructured interviewing, there are no set of standardized questions. The discussion can travel in any direction depending on the interviewer. Due to lack of patter, these interviews are hard to crack.
Case Studies

Case studies do not go along with a single method. There are various methods which are being used for observing even the minute details. It can be called as the summation of the direct method, unstructured interviews etc. The quantitative and qualitative approaches a given situation in an entirely different way. For example, quantitative methods are based on mathematical numbers, graphs, and statistics. But because of this method, much information is lost accounting for little information as compared to the qualitative method. Quantitative analysis is fact-driven but the facts can change anytime but they are mostly copied from earlier records, whereas qualitative analysis is observation-driven, its data can be changed accordingly which is its biggest advantage over the other.

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.

Secondary resources are the data which are mainly records in any form. For example, any old book can provide much information about the time period comes under secondary resources. There is no direct information but mainly statistics, graphs, old research works, or historical books.

MORE METHODS OF QUALITATIVE AND QUANTITATIVE ANALYSIS:-

Participant and Quasi-Participant Observation

It has been proved for a long time that observation helps in collecting data as well as result in accurate analysis. Observation contains two major functions viz. causes and effects. The observation is categorized in two ways viz. controlled and uncontrolled, active and passive.

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.

In passive observation, the researcher observes everything from a distant place without getting noticed. For example mother-son duo small gestures can be easily noticed by him/her.

Controlled observations are those matter of solicitation in which things can be brought under control anytime. For example, knowing that someone is observing me I can easily change my reactions.

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.

Informal interviews are not much in trend but the other three are practised at a rapid rate.

Questionnaire

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.

The schedule is entirely based on the way an interviewer seek things. The questionnaire set is solved by a person in front of the researchers. Thus the question does not affect much, but the perspective of the researcher does. There are many types of schedule:-

Rating Schedules – This kind of schedules generally come under the HR department. The opinions, ways of accepting or rejecting things, or habits are observed keenly.
Document Schedules – As the name suggests, it generally involves the paperwork. For example in criminology, criminal’s history is studied. Case studies are also popular, for example how to transform a city into the smart city.
Evaluation Schedules – Quantitative analysis for example data collection is a primary objective of this schedule. For example, if a company arrives at placement, the students collect every data, for example, the company position, job profile, CTC etc.
Observation Schedules – The researcher will observe everybody’s intention, either by involving in any activity or by being aloof.
Interview Schedules – The researcher freely asks respondents any question and after deciding their confidence, time to think, IQ etc is judged.

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

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3 Sociological Research Methods

Learning Objectives

In this chapter you will:

Compare and contrast social science research methods.
Evaluate strengths and weaknesses in research design.

Research methods are the techniques and tools used to collect data and systematically study the social world (Conley 2019). Sociological research methods are typically grouped into two categories:

Quantitative methods involve a large number of research participants and produce numeric data that are analyzed statistically to test hypotheses.
Qualitative methods involve small samples of research participants and produce textual or visual data that are thematically analyzed to uncover deeper meanings.

Let’s look at some examples of each.

Quantitative Research Methods

First, survey research is a common example of quantitative methods used in social science research. Surveys contain fixed-answer questions that can be converted to a numeric value for statistical analysis (Bhattacherjee 2012). Some researchers use existing data from nationally representative surveys, like the American Community Survey conducted by the U.S. Census Bureau.

Other researchers create their own surveys to collect data on locally relevant issues. For example, in the Social Science Lab we recently asked residents in the City of Muskegon, Michigan about their impressions of the Muskegon Lake restoration, including current water quality in the lake.

One strength of quantitative methods is that research results can be generalized to a larger group of people beyond the study sample, giving us information about a broad cross-section of a population of interest. However, because quantitative data must be expressed in numbers, they can often tell us what people think or do but not why. To get at the “why” that underlies human beliefs and behaviors, we need qualitative research.

Qualitative Research Methods

There are a variety of qualitative methods that help uncover the meanings behind people’s actions.

In observation research , researchers spend time observing a group of people to learn about the cultural norms and motives that drive particular actions. They may participate in that culture, or they may observe without participating. One example is Matthew Desmond’s (2016) work, Evicted. In this book, Desmond describes his participant observation of individuals experiencing eviction to uncover barriers to housing security faced by low-income families.

In-depth interviewing , where researchers ask participants a series of questions intended to encourage lengthy discussion of the research topic, is also a commonly used qualitative research method. Interview research has been used to get at the heart of complex problems. In the example “Why Detroit Residents Pushed Back Against Tree-Planting,” (below) interview research helped city planners understand why Detroit residents rejected free tree plantings in their neighborhood (Mock 2019).

Why Detroit Residents Pushed Back Against Tree-Planting (Bloomberg)

Content analysis of texts or written communication is used to identify patterns in how people talk about or make sense of pressing social issues. For example, check out the analysis linked below, which discusses what memes tell us about how people in work-from-home, professional occupations experienced the coronavirus pandemic.

What do memes tell us about self and time during the pandemic? (contexts)

Although this is hardly an exhaustive list of research methods, visual methods also deserve a mention. Visual methods consist of a variety of research methods that systematically analyze art, drawings, photography, videos, or maps created by a population of interest to the research questions. For example, follow the link below to listen to Jill Weinberg describe her use of post-it notes to generate data and dialogue.

Jill Weinberg on Post-It Notes as a Visual Method (The Society Pages)

Quality Control

When you are evaluating research reported in the news or on social media, it’s important to think critically about the research methodology used to collect the data being reported. This is particularly important when reading reports of quantitative studies, such as surveys, because numbers are powerful. As a result, the results of survey research and public opinion polling are commonly cited in popular press. As a general rule, the more outlandish the findings, the closer you ought to scrutinize the research methods. This may require some digging! Here are a few things to keep in mind.

The goal of quantitative research is often to test the relationship between two or more variables , which are characteristics – like an attitude, trait, or behavior – that have two or more potential values. For example, the variable “education level” has several categories: less than high school, high school graduate, some college, two-year technical degree, bachelor’s degree, or graduate degree. Assigning measurements to concepts relevant to a particular study is known as the process of operationalization . The way that a concept is operationalized has important bearings on the validity of the measure. Valid measures operationalize variables such that the variable measures what is intended to be measured.

Bear with me. The Yale Program on Climate Change Communication is a group of social scientists studying public opinions and behaviors related to climate change. Their goal is to inform communication strategies used by public and private organizations engaging in climate education (YPCCC 2022). The program developed a four-question “SASSY” survey to classify people into one of six groups, from “alarmed” to “dismissive,” according to their level of concern about climate change. Use the link at right to access the survey and click through the questions (you don’t have to answer honestly, just screen the survey questions).

Map showing distrubution of CRSI Scores across the U.S. (2000-2015). Scores are lower in the south east, in the middle in the midwest, in New England and central, and highest in the west and far north (except California coast).

It therefore might be totally rational for a SASSY respondent from Duluth, Minnesota to say they expect climate change to harm them personally “not at all,” even as they hold a great deal of concern about how climate change will impact the ocean environment, or people living in remote corners of the Andes Mountains. How valid, then, is the third question as a measure of climate concern? Measuring complex concepts is difficult and information consumers should be on the look-out for measurements that poorly represent the concept they were intended to operationalize.

Reliability

Additionally, consider whether or not the research methods used are likely to be reliable , meaning that a systematic, replicable process was used to collect and analyze data. Reliability is particularly important in quantitative research, and difficult to achieve in qualitative research. That’s because the interviewer themselves – their ability to read non-verbal cues or be quick on their feet with follow-up questions – has a large influence on the quality and quantity of data collected from the interview participant. In contrast, the researcher may not interact very much at all with survey participants. In survey research, it is important that each potential participant receives the seem solicitation, the same instructions, the same questions, follows the same procedure to complete their questionnaire, and that all data are analyzed in the same way using a procedure that can be described to and repeated by anyone else.

Generalizability

Finally, carefully consider whether the research has been designed so that it is generalizable to the broader groups the study claims to represent. For example, most surveys include a sample of the population subset of interest to the researcher. In the example shown earlier of the Muskegon Lake residential survey, the research population we were interested in was all residents who live in the City of Muskegon, who are a specific subset of the larger population of Michigan residents. Rather than mailing a survey to all 30,000 residents in Muskegon, which would be very expensive, we obtained a list of all property owners in the city and used a random number generator in Excel to draw a random sample of 1,200 residents, who were mailed a copy of the survey. This is known as probability sampling, and is often simply called random sampling.

Hear it from the experts:

In order for survey researchers to make claims about the broader population from which the sample of people who completed the survey was drawn – otherwise known as generalizing their results –all members of a research population must have had an equal chance of being selected to complete the survey. So, if I stood outside of the Walmart in Norton Shores and asked the first 1,200 people I saw to complete the survey, that wouldn’t be a random sample because only people who shop at Walmart on the particular day and time I conducted my survey had a chance of being selected for participation.

Likewise, if I posted a link to the survey on social media sites of various community organizations – a REALLY POPULAR way of soliciting survey participants – the study results would only apply to people connected to those particular social media networks, who likely differ from the average Muskegon resident in some way. They may be more civically engaged, more interested in lake recreation, wealthier and more influential, or otherwise distinctive from the vast majority of Muskegon residents who are not following the social media accounts of the groups I’ve asked to share my survey link. In either case, my research results could not be accurately generalized to the broader population of Muskegon residents. This is a common flaw of research design that you should be on the lookout for as you interpret research reported in popular and news media.

Generalizability is typically not a goal in qualitative research, which usually relies on a variety of non-probability sampling techniques to solicit participants. However, the best qualitative research (IMHO) strategically recruits participants who represent diverse viewpoints on the topic of the study, to ensure that a broad range of perspectives and truths are included in their data. Reader beware of studies that represent dynamic issues through the eyes and voices of a single stakeholder group. It’s nearly always more complicated than that!

Sociological research helps us understand why people believe or act in particular ways. This information is critical for developing programs, policies, and plans members of the public will support, creating solutions that effectively deal with societal problems, and analyzing where processes intended to meet public needs fall short or break down. Not all research is created equal, and it’s important to be able to identify faulty elements of research design – such as errors in validity, reliability, and generalizability – that compromise the conclusions drawn by the researcher. When done well, sociological research can reveal the complexity of social phenomena, highlighting divergent viewpoints on a given issue and developing a nuanced understanding of the social world.

Berg, Bruce L. 2007. Qualitative Research Methods for the Social Sciences, 6th edition . Boston, MA: Pearson.

Bhattacherjee, Anol. 2012. “Social Science Research: Principles, Methods, and Practices.” Textbook Collections. 3 (http://scholarcommons.usf.edu/oa_textbooks/3).

Conley, Dalton. 2019. You May Ask Yourself: An Introduction to Thinking Like a Sociologist, 6th edition. New York: W.W. Norton.

Desmond, Matthew. 2016. Evicted: Poverty and Profit in the American City. New York: Broadway Books.

Flaherty, Michael G. and Cosima Rughinis. 2021. “What Do Memes Tell Us about Self and Time During the Pandemic?” Contexts. Accessed 21 July, 2022. (https://contexts.org/articles/what-do-memes-tell-us-about-self-and-time-during-the-pandemic/).

Green, Kyle. 2017. “Jill Weinberg on Post-It Notes as a Visual Method. Give Methods a Chance. Accessed 21, July 2022. (https://thesocietypages.org/methods/2017/03/18/jill-weinberg-on-post-it-notes-as-a-visual-method/).

Mock, Brentin. 2019. “Why Detroit Residents Pushed Back Against Tree-Planting.” Bloomberg. Accessed 21, July 2022. (https://www.bloomberg.com/news/articles/2019-01-11/why-detroiters-didn-t-trust-city-tree-planting-efforts).

US EPA. 2020. “Development of a Cumulative Resilience Screening Index (CRSI) for Natural Hazards: An Assessment of Resilience to Acute Meteorological Events and Selected Natural Hazards.” Accessed 21 July, 2022. (https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=350154&Lab=CEMM).

“Why Social Science?” Consortium of Social Science Associations. Accessed 21 July, 2022 ( https://www.whysocialscience.com/ ).

Yale Program on Climate Change Communication. 2022. “About the Program.” Accessed 21 July, 2022. ( https://climatecommunication.yale.edu/about/the-program/ ).

Research techniques involving a large number of research participants and producing numeric data that are analyzed statistically to test hypotheses (Conley 2019).

Research techniques involving small samples of research participants and producing textual or visual data that are thematically analyzed to uncover deeper meanings (Conley 2019).

A quantitative research method using fixed-answer questions that can be converted to a numeric value for statistical analysis (Bhattacherjee 2012).

A qualitative research method involving prolonged observation of and interaction with a group of people (Berg 2007).

A qualitative research method in which a researcher asks participants a series of questions to inspire lengthy discussion and dialogue about the research topic (Berg 2007).

A qualitative research method involving identification of thematic patterns in written communication or texts (Berg 2007).

A qualitative research method in which research participants create visual artifacts (i.e., art, drawings, photography, videos, or maps) that are analyzed by the researcher (Green 2017).

Measurable characteristics that have two or more potential values (Conley 2019).

The process of assigning measurements to concepts (Conley 2019).

Degree to which variables accurately measure the concept they were intended to measure (Conley 2019).

The degree to which the process used to collect and analyze data can be replicated (Conley 2019).

The degree to which data from a research sample can be used to infer conclusions about the broader population from which the sample was drawn (Conley 2019).

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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.

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Quantitative Research Methods (Online Lesson)

Last updated 17 Aug 2021

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In this online lesson students are introduced to the strengths and limitations of a number of research methods that tend to produce quantitative data. Several key terms are covered (e.g. reliability, representativeness, sampling frame, pilot study, target population, hard and soft statistics).

WHAT YOU'LL STUDY IN THIS ONLINE LESSON

Which research methods commonly produce quantitative data
Strengths and limitations of questionnaires / social surveys
Strengths and limitations of experiments
Strengths and limitations of official statistics
Other sources of quantitative data

HOW TO USE THIS LESSON

Follow along in order of the activities shown below. Some are interactive game-based activities, designed to test your understanding, analysis and evaluation of research methods. Others are based on short videos, that include activities for you to think about and try at home.

If you would like to download a simple PDF worksheet to accompany the video activities, you can download it here . You can print it off and annotate it for your own notes, or make your own notes on a separate piece of paper to add to your school/college file.

Activity 1: Online game

A quick activity to review learning from the previous lesson (Introduction to Theory & Methods) Watch out for possible red herrings and curveballs!

Activity 2: Introduction to Quantitative Methods

This video outlines the methods that will be considered in this lesson. It also introduces students to sampling methods, as well as introducing an ongoing home research task.

Activity 3: Questionnaires / Social Surveys

This video explores social surveys / questionnaires and their strengths and weaknesses. You may want to use this free survey software to assist with one of the activities.

Activity 4: Online game

An activity to test your understanding of social surveys / questionnaires.

Activity 5: Experiments

In this video, you will investigate experiments and their strengths and weaknesses for sociological researchers.

Activity 6: Online Game

An activity to test your understanding of sociological experiments.

Activity 7: Official Statistics

In this video, you will investigate official statistics and their strengths and limitations for sociologists.

Activity 8: MCQ Quiz

This quiz will test your knowledge and understanding of all three methods explored in this lesson. Keep your score.

Activity 9: Exam-style questions

Outline & explain two strengths of using questionnaires for social research (10 marks)

Outline & explain two limitations of using official statistics for social research (10 marks)

Further useful links:

Official statistics on families & households

Police statistics

Additional Teacher Guidance

40 minutes in total of guided video

20 to 25 minutes (throughout the videos) of "thinking time" and short activities

4 interactive games - you could ask your students to submit their score to you for Activity 8

An exam-style written task (two ten-mark questions) which we would expect to take approximately 30 minutes of writing time, and which you could ask students to submit for assessment.

If students complete all tasks fully, this is around 2 hours work.

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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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Do you have a background in quantitative methods and an interest in the topic of educational inequality? Are you enthusiastic about the opportunity to be part of a larger team and develop and conduct mixed methods research on teacher beliefs and selection practices in primary education?

The Institutions, Inequalities and Life courses research group at the Department of Sociology of the University of Amsterdam is looking for a PhD candidate. The PhD candidate will become member of the team working on the project “Making or breaking the class ceiling: growth-affording teacher beliefs and practices in a selective school system”, and will work in close collaboration with and under the supervision of Dr Sara Geven and Prof Thijs Bol.

Job/Project description

In the Netherlands, students are sorted into different educational programs (i.e., ability tracks) in secondary school. Anticipating upon this selection, this project focuses on how primary school teachers (may) already sort pupils into ability groups before this formal selection moment. For instance, some primary schools may use ability-based instruction groups for subjects like mathematics or reading. There is still a limited understanding of the prevalence of such informal selection practices, and their impact on (inequality in) educational outcomes in Dutch primary education. In this project we study how teachers’ growth-affording beliefs about pupils relate to selection practices and, eventually, pupils’ educational outcomes. Selection practices may relate to teachers’ beliefs about whether academic ability and talent are innate or can be developed through effort. In the second part of the project, we examine whether an intervention can enhance (growth-affording) beliefs and practices among teachers. Moreover, we wish to examine whether this intervention can enhance equal learning opportunities, by studying if it may (particularly) enhance the educational outcomes of students from disadvantaged backgrounds. To do so, we look at the beliefs and performance (growth) of students from different socioeconomic backgrounds.

This PhD project is part of a larger project funded by the NRO, called “Making or breaking the class ceiling: growth-affording teacher beliefs and practices in a selective school system”. For this larger project, one other PhD candidate will be employed at the Department of Educational Sciences at the University of Amsterdam who will work on selection practices in secondary education. The current project will employ a mixed methods design for which different data sources will be used. First, we will collect interview data to map how teachers in primary education sort students into different ability groups before the formal moment of selection into secondary ability tracks takes place, and what beliefs underlie these practices. Second, we rely on quantitative student data that are already collected. Third, we will develop and implement an intervention study on teacher beliefs and their effects on (inequality in) educational outcomes. Given the Dutch context where the interviews will be done and the intervention will be implemented, there is a strong preference for a candidate who masters the Dutch language.

What are you going to do

In this position you will:

contribute to the design and collection of interview data on selection practices in primary education;
analyze secondary data on selection practices in education using quantitative methods;
help with the design and implementation of an intervention focused on the mindsets of teachers, and study its effects;
independently do research and write academic articles;
assist with administrative tasks and project management;
be involved in teaching in the BA programme of Sociology at the University of Amsterdam (10% of the working time).

What do you have to offer

We are looking for a candidate with the following credentials:

a completed (Research) Master’s degree in a relevant field (e.g., sociology, educational sciences, (developmental) psychology). The degree must have been obtained by the employment starting date;
excellent oral and written communication skills in English;
oral and written proficiency in Dutch;
command of advanced quantitative research methods, as well as the willingness to further develop these skills;
command of interview methods, or the willingness to further develop these skills;
independent thinking and critical analytical skills;
ability and willingness to collaborate in a diverse team with partners outside of academia too;
ability to finish the dissertation in time: i.e. strong skills in project management, flexibility, a proactive approach;
willingness to live in, or within a commuting distance from, Amsterdam for the duration of the contract.
theoretical expertise in the field of inequality in education;
experience with collecting interview data.

What can we offer you

The position concerns temporary full-time employment of 38 hours per week (1,0 fte). The initial employment term will be for one year, with a probationary period of two months. Following a positive assessment and barring altered circumstances, this term will be extended by a maximum of three years (for a total duration of four years), which should result in the conferral of a doctorate.

Your salary will be €2,770 gross per month in the first year and will increase to €3,539 in the final year, based on full-time employment of 38 hours per week as per Collective Labour Agreement of Dutch Universities . For this position the University Job Classification profile ‘promovendus’ applies. We additionally offer an extensive package of secondary benefits, including 8% holiday allowance and a year-end bonus of 8.3%.

Because we value your continued personal development and professionalisation, we also offer excellent opportunities for study and development. You will follow a curriculum with other AISSR PhD candidates, and will be able to join the ICS-Graduate School. You will also have the opportunity to attend training courses and both national and international conferences and workshops. You will be tasked with teaching Bachelor's students.

What else do we offer

a position in which initiative and input are highly valued;
the opportunity to conduct cutting-edge research in a dynamic and vibrant environment in which you will also engage with people in the field;
the opportunity to conduct a research visit abroad;
financial support for academic conferences and summer schools;
the opportunity to follow a wide range of graduate courses in social sciences and research methods;

The University of Amsterdam is the largest university in the Netherlands, with the broadest spectrum of degree programmes. It is an intellectual hub with 39,000 students, 6,000 employees and 3,000 doctoral students who are all committed to a culture of inquiring minds.

Please follow the links to learn more about the Institutions, Inequalities and Life courses research group, the Department of Sociology , the ICS Graduate School , and the AISSR .

Want to know more about our organisation? Read more about working at the University of Amsterdam.

Do you have any questions or do you require additional information? Please contact:

Thijs Bol, Professor in Sociology, [email protected]

Job application

The Faculty of Social and Behavioral Sciences strives for and puts conscious efforts into having a work and academic environment that is inclusive. We commit to providing the grounds for equal treatment and empowering you to become a full participating member of our academic community, regardless of your background, race, sexual orientation, gender identity, disability and/or age. We especially invite members from historically disadvantaged/under-represented groups to apply. If this vacancy speaks to you, but you are uncertain whether you meet all requirements, please do apply. Given the department’s commitment to diversity, we strongly encourage applications from all qualified candidates, and specifically from people with backgrounds underrepresented in academia.

Do you recognize yourself in the job profile? Then we look forward to receiving your application by 12 May 2024.

You may apply online by using the link below. Please upload the following materials combined into a single PDF document with your name and the vacancy number on the next page in the field labeled 'CV/Resume' (not the motivation letter field):

a maximum two-page cover letter that outlines your preparation and motivation to pursue this job. Please be as specific as possible in describing how you meet the selection criteria. If you do not meet all of the criteria yet, please explain how you intend to acquire the required skill;
(unofficial) undergraduate and graduate transcripts;
the contact details of two academic references who know you well.

Please do NOT include any other materials at this stage. Only short-listed candidates will be requested to submit additional materials such as recommendation letters, writing samples, and/or a short draft of the PhD proposal. The committee expects the interviews to take place on 23 May.

The UvA is an equal-opportunity employer. We prioritize diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity. If you encounter Error GBB451/ GBC451, reach out to our HR Department directly. They will gladly help you continue your application. No agencies please.

The University of Amsterdam is one of the largest comprehensive universities in Europe. With some 40,000 students, 6,000 staff, 3,000 PhD candidates, and an annual budget of more than 850 million euros, it is also one of Amsterdam’s biggest employers. Deze bedrijfspagina is automatisch gegenereerd en bevat daarom nog weinig informatie. Je vindt meer informatie over ‘bedrijfsnaam’ op hun website: ‘’Carrierewebsite’’

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ReviseSociology

A level sociology revision – education, families, research methods, crime and deviance and more!

Concepts in Quantitative Sociological Research

Concepts are the building blocks of theory, and are the points around which social research is conducted.

Concepts are closely related to the main sociological perspectives , and some of the main concepts developed by different perspectives include:

Functionalism – social integration and anomie
Marxism – social class and alienation.
Feminism – gender and patriarchy
Interactionism – labelling and discrimination
Postmodernism – identity.

Within sociology, one might even say that there’s a more ‘fundamental’ layer of concepts that lie behind the above – such as ‘ society’, ‘culture’ and ‘socialization ‘, even ‘sociology’ itself is a concept, as are ‘research’ and ‘knowledge’.

Concepts also include some really ‘obvious’ aspects of social life such as ‘family’, ‘childhood’, ‘religious belief’, ‘educational achievement’ and ‘crime’. Basically, anything that can be said to be ‘socially constructed’ is a concept.

Each concept basically represents a label that researchers give to elements of the social world that strikes them as significant. Bulmer (1984) suggests that concepts are ‘categories for the organisation of ideas and observations’.

Concepts and their measurement in quantitative research

If a concept is to be employed in quantitative research, a measure will have to be developed for it so it can be quantified.

Once they have been converted into measures, concepts can then take the form of independent or dependent variables. In other words, concepts may provide an explanation of a certain aspect of the social world, or they may stand for things we want to explain. A concept such as educational achievement may be used in either capacity – we may explore it as a dependent variable (why some achieve fewer GCSE results than others?) Or: as an independent variable (how do GCSE results affect future earnings?).

Measures also make it easier to compare educational achievement over time and across countries.

As we start to investigate such issues we are likely to formulate theories to help us understand why, for example, educational achievement varies between countries or over time.

This will in turn generate new concepts, as we try to refine our understanding of variations in poverty rates.

Why Measure Concepts?

It allows us to find small differences between individuals – it is usually obvious to spot large differences, for example between the richest 0.1% and the poorest 10%, but smaller once can often only be seen by measuring more precisely – so if we want to see the differences within the poorest 10%, we need precise measurements of income (for example).
Measurement gives us a consistent device, or yardstick for making such distinctions – a measurement device allows us to achieve consistency over time, and thus make historical comparisons, and with other researchers, who can replicate our research using the same measures. This relates to reliability.
Measurement allows for more precise estimates to be made about the correlation between independent and dependent variables.

Indicators in Quantitative Social Research

Because most concepts are not directly observable in quantitative form (i.e. they do not already appear in society in numerical form), sociologists need to devise ‘indicators’ to measure most sociological concepts. An indicator is something that stands for a concept and enables (in quantitative research at least) a sociologist to measure that concept.

For example….

We might use ‘Average GCSE score’ as an indicator to measure ‘educational achievement’.
We might use the number of social connections an individual has to society to measure ‘social integration’, much like Hirschi did in his ‘ bonds of attachment theory ‘.
We might use the number of barriers women face compared to men in politics and education to measure ‘Patriarchy’ in society.

NB – there is often disagreement within sociology as to the correct indicators to use to measure concepts – before doing research you should be clear about which indicators you are using to measure your concepts, why you are choosing these particular indicators , and be prepared for others to criticize your choice of indicators.

Direct and Indirect indicators

Direct indicators are ones which are closely related to the concept being measured. In the example above, it’s probably fair to say that average GCSE score is more directly related to ‘educational achievement’ than ‘bonds of attachment’ are to ‘social integration’, mainly because the later is more abstract.

How sociologists devise indicators:

There are a number of ways indicators can be devised:

through a questionnaire
through recording behaviour
through official statistics
through content analysis of documents.

Using multiple-indicator measures

It is often useful to use multiple indicators to measure concepts. The advantages of doing so are three fold:

there are often many dimensions to a concept – for example to accurately tap ‘religious belief’ questionnaires often include questions on attitudes and beliefs about ‘God’, ‘the afterlife’, ‘the spirit’, ‘as well as practices – such as church attendance. Generally speaking, the more complex the concept, the more indicators are required to measure it accurately.
Some people may not understand some of the questions in a questionnaire, so using multiple questions makes misunderstanding less likely.
It enables us to make more nuanced distinctions between respondents.

Measuring the effectiveness of measures in quantitative social research

It is crucial that indicators provide both a valid and reliable measurement of the concepts under investigation.

2 thoughts on “Concepts in Quantitative Sociological Research”

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Evaluate strengths and weaknesses in research design. Research methods are the techniques and tools used to collect data and systematically study the social world (Conley 2019). Sociological research methods are typically grouped into two categories: Quantitative methods involve a large number of research participants and produce numeric data ...
Interpretive Quantitative Methods for the Social Sciences
Abstract. Quantitative social science has long been dominated by self-consciously positivist approaches to the philosophy, rhetoric and methodology of research. This article outlines an alternative approach based on interpretive research methods. Interpretative approaches are usually associated with qualitative social science but are equally ...
7.3: Sampling in Quantitative Research
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.
Quantitative Research Methods (Online Lesson)
Quantitative Research Methods (Online Lesson) In this online lesson students are introduced to the strengths and limitations of a number of research methods that tend to produce quantitative data. Several key terms are covered (e.g. reliability, representativeness, sampling frame, pilot study, target population, hard and soft statistics).
Scientific Quantitative Methodology in Sociology
Social Facts. The first rule of Positivist methodology is to consider social facts as things which means that the belief systems and customs of the social world should be considered as things in the same way as the objects and events of the natural world. According to Durkheim, some of the key features of social facts are: they exist over and ...
Statistical Methods in Sociology (QCR)
Most research in sociology is quantitative, and it is important for students to be able to critically evaluate published quantitative research. Ideally, students should also be able to conduct empirical research involving statistical methods. This course provides the foundation for both goals. The course focuses specifically on how to determine,...
The Methodological Divide of Sociology: Evidence from Two Decades of
Past research indicates that Sociology is a low-consensus discipline, where different schools of thought have distinct expectations about suitable scientific practices. This division of Sociology into different subfields is to a large extent related to methodology and choices between qualitative or quantitative research methods.
Quantitative and Qualitative Methods in sociology
Definition. 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 ...
Position in Sociology of Education
command of advanced quantitative research methods, as well as the willingness to further develop these skills; command of interview methods, or the willingness to further develop these skills; independent thinking and critical analytical skills; ability and willingness to collaborate in a diverse team with partners outside of academia too;
Concepts in Quantitative Sociological Research
Concepts are the building blocks of theory, and are the points around which social research is conducted. Concepts are closely related to the main sociological perspectives, and some of the main concepts developed by different perspectives include: Functionalism - social integration and anomie. Marxism - social class and alienation.

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Quantitative Methods in Sociological Research by Erin Leahey LAST REVIEWED: 13 November 2018 LAST MODIFIED: 27 July 2011 DOI: 10.1093/obo/9780199756384-0044

2.2 Research Methods

Field Research

Sociology in the Real World

Participant Observation

Ethnography

Institutional Ethnography

Sociological Research

Experiments

Secondary Data Analysis

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What Is Quantitative Research? | Definition, Uses & Methods

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A Quick Guide to Quantitative Research in the Social Sciences

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Alexandra Killewald

Soc 156: Quantitative Methods in Sociology

Quantitative Methods

Jeremy Freese

David Grusky

Michelle Jackson

Michael Rosenfeld

Jan G. Voelkel

Sociological Research Methods: Qualitative and Quantitative Methods

Research methods are categorized into Qualitative and Quantitative methods .

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7.3: Sampling in Quantitative Research

Probability Sampling

Types of Probability Samples

Quantitative Research Methods (Online Lesson)

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