types of research in social science

Social Science Research: Meaning, Significance, Process, Examples

Social science research: overview

Introduction: A systematic and step by step search into a phenomenon is known as research. As its name itself define its meaning, that is Re-search. A new investigation into a subject that may be an existing body of knowledge, we contribute to it through a new investigation. It is termed research. It is a scientific investigation followed by various methods and techniques. “D. Slesinger and D. Stephenson define social science research as the manipulation of things, concepts, or symbols to generalize to extend, correct, or verify knowledge whether that knowledge aids in the construction of theory or the practice of an art”. We can also simply said that it is a gift to the advancement and enhancement of already known pieces of information. Social science researchers also follow scientific methods and techniques to conduct research.

Significance of social science research

Research process.

Social science research is done in various steps. These steps or actions is inevitable to carry out the entire research. The various steps that involve in an investigation are;

In the research process, this is the first and the most crucial step. All other steps are depending on this step. The topic or the research problems tells you and others what you intended to study or your destination of research. Mainly there are 2 different kinds of topics one is related to states of nature and the other is related to the relationship between variables. There are mainly two kinds of variables one is a dependant variable and the other is an independent variable. The dependant variables are variables that depend on the independent variable. For example, if we study the unemployment among youth; we can say income, family background, education qualifications; the experience can be dependent variables that depend on the unemployment among youth which is an independent variable. After you select a topic or general topic, the next thing you must consider is to narrow down the general topic into a more specific one. It is not suitable or accurate to research a general topic. Because it is will be difficult to study a wide topic. Therefore it is necessary to determine the specific topic you wish to study or research. For example, if you want to study marriage you have to narrow it down to a more specific one; you can choose to study Catholic marriage customs in some specific geographical areas. It will be easier to study rather study marriage in a specific area. By doing like this one will get the most fruitful and reliable information that will enhance the current knowledge that existing in your field of study.

The reviewing literature provides you most thoughtful ideas, discoveries and a new dimension to your study. So it is really necessary to put your best to do a literature survey. Because above all the sufferings, It will guide your research.

In short, the literature review helps you to provide;

After the literature survey, the next step is developing a hypothesis for your research. Hypotheses are tentative assumptions made to test their logical and empirical consequences. It provides a focal point for research. For example, if the topic is related to Gender we can make a hypothesis ‘Women are emotional than men’; this is an assumption made by the researcher and this assumption is tested through the research. We can test this after analyzing the data collected. The hypothesis will help the researcher to concentrate on the topic and to keep the researcher on the same route without any diversion. It also shows the researcher what kind of data is needed and what methods of data analysis should follow.

In social science research, the whole unit under the study is known as the universe or population. For example, if your research topic is the Unemployment of youth in Mexico. The youth in Mexico will be the universe or population of your study. A complete enumeration or study of the entire population or universe means census enquiry. For example, the census took place in India every ten years is an example of the census. But in research, we don’t need to enumerate the entire population under study. Or in other terms, we need to select some units from the entire population under study, that is, we need to select the samples rather than to study the entire population. There are two kinds of sampling, one is probability sampling and the other is non-probability sampling. In probability sampling, the entire population gets an equal chance to be drawn but in non-probability sampling, the entire population does not get an equal chance to be drawn. Simple random sampling, stratified random sampling, systematic sampling are among the probability sampling techniques. In non-probability sampling, the data collected from convenience sampling, judgmental sampling, quota sampling, etc. The result of your data depends on the characteristics and attributes of your selected samples. The selected samples should provide you with the necessary and accurate data. Whether you select probability sampling or non-probability sampling is depends on the topic you selected. When you select a rare and sensitive population that is hard to get, you can choose a non-probability sampling of your choice and your respondent’s confidentiality. The sampling method will eliminate unwanted costs and travelling. It will save you time.

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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Authors: Tonja R. Conerly, Kathleen Holmes, Asha Lal Tamang
Publisher/website: OpenStax
Book title: Introduction to Sociology 3e
Publication date: Jun 3, 2021
Location: Houston, Texas
Book URL: https://openstax.org/books/introduction-sociology-3e/pages/1-introduction
Section URL: https://openstax.org/books/introduction-sociology-3e/pages/2-2-research-methods

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Basic Research Strategies for the Social Sciences: Research Methods

Research Strategies
Research Methods
Systematic Reviews vs. Literature Reviews
Background Information
Evaluate Your Sources
Scholarly vs. Non-scholarly Articles
Finding Journals
Journal Articles
SU Libraries' Catalog
Maps & Statistical Sources
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Sage Research Methods Online (SRMO)

SAGE Research Methods Online

Sage Research Methods Online (SRMO). SRMO provides access to information about research methods compiled from a variety of Sage publications, including books/handbooks, articles, and the “Little Green Book” series, Quantitative Applications in the Social Sciences . SRMO is searchable and browsable by author, and it includes a methods map, as well as video tutorials. Results can be refined to focus on specific academic disciplines of interest.

Great resource for learning more about what comprises a specific research method, with a view into how that method was applied within actual published scholarly literature.

analysis of variance (ANOVA)
ethnography
focus groups
mixed methods
narrative analysis
qualitative research
quantitative data analysis
social network analysis
structural equation modeling
time-series analysis
visual representations
... and more

Research Methodologies

There are a variety of methods you can adopt for your research strategy, depending on your subject area or the outcome of your research. Research methodology will differ depending on whether:

you are doing an empirical study, using quantitative data or qualitative information, or mixed methods approach
If you are seeking very current sources, or
historical research
critical analysis

Your strategies will be different as will the type of information sources you will seek and find.

See some databases below that offer examples of research methods, datasets or cases:

Sage Research Methods: Data Visualization Video, text, and datasets to teach researchers the fundamentals of data visualization and design.
Sage Research Methods: Foundations Introductory information about research methods and design.
SAGE Research Methods Cases Teaching cases in which a variety of research methods are used in a number of social sciences subject areas. Cases are incorporated into SAGE Research Methods Online.
SAGE Research Methods Datasets Datasets for teaching qualitative and quantitative research methods. Datasets are incorporated into SAGE Research Methods Online, and include sample sets, with a description of the research project and instructions regarding the method.
SAGE Research Methods Online Information about research methods and design; includes Sage Datasets and Sage Cases, and the qualitative and quantitative methods series, "Little Green Books" and “Little Blue Books.”

Research Integrity

SU - Office of Research and Integrity The Office of Research and Integrity provides administrative services to university researchers to facilitate research and ensure regulatory compliance with applicable federal regulations, laws and University policies, including administrative support and regulatory advisement to the University’s Institutional Review Board (IRB) and Institutional Animal Care and Use Committee (IACUC).

Research Methods for the Social Sciences: An Introduction

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Valerie Sheppard, JIBC

Last Update: 2024

Publisher: BCcampus

Language: English

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Accessibility Statement
About This Book
Chapter 1: Introduction to Research Methods
Chapter 2: Ethics in Research
Chapter 3: Developing a Research Question
Chapter 4: Measurement and Units of Analysis
Chapter 5: The Literature Review
Chapter 6: Data Collection Strategies
Chapter 7: Sampling Techniques
Chapter 8: Data Collection Methods: Survey Research
Chapter 9: Analysis Of Survey Data
Chapter 10: Qualitative Data Collection & Analysis Methods
Chapter 11: Quantitative Interview Techniques & Considerations
Chapter 12: Field Research: A Qualitative Research Technique
Chapter 13: Unobtrusive Research: Qualitative And Quantitative Approaches
Chapter 14: The Research Proposal
Chapter 15: Sharing Your Research
Chapter 16: Reading and Understanding Social Research
Chapter 17: Research Methods in the Real World
List of Links

Ancillary Material

About the book.

This textbook provides a broad overview of research methods utilized in sociology. It will be of particular value for students who are new to research methods.

About the Contributors

Valerie Sheppard , JIBC

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Using Science as Evidence in Public Policy (2012)

Chapter: appendix a: selected major social science research methods: overview.

Appendix A Selected Major Social Science Research Methods: Overview

T he social sciences comprise a vast array of research methods, models, measures, concepts, and theories. This appendix provides a brief overview of five common research methods or approaches and their assets and liabilities: experiments, observational studies, evaluation, meta-analyses, and qualitative research. We close with a discussion of new sources of data. We begin with a brief comment on cause and effect.

To inform public policy, researchers often frame their studies in terms of causal conclusions and reason from an intervention to its intended outcomes. Many types of research methods are used for this purpose, as well as statistical analyses.

Research that can reach causal conclusions has to involve well-defined concepts, careful measurement, and data gathered in controlled settings. Only through the accumulation of information gathered in a systematic fashion can one hope to disentangle the aspects of cause and effect that are relevant to a policy setting. Statistical methodology alone is of limited value in the process of inferring causation.

The literature on causality spans philosophy, statistics, and social and other sciences. Our use here is consistent with the recent literature describing causality in terms of counterfactuals, interventions or manipulation, and probabilistic interpretations of causation.

EXPERIMENTS

In the simplest study of an intervention, one group of subjects who receive the intervention (the treatment group ) is compared with another group of subjects (the control group ) who do not. When the control group receives no other intervention, it serves to depict the counterfactual : what would happen in the absence of the intervention. Many studies, however, are more elaborate and may involve multiple interventions and controls.

An experiment is a study in which the investigator controls the selection of the subjects who may receive the intervention and assigns them to treatment and control groups at random. Experiments can be conducted in highly controlled settings, such as in a laboratory, or in the field, such as at a school, so as to better reflect the context in which an intervention would be implemented in practice. The former assess efficacy , or whether the intervention produces the intended effect. The latter, called randomized controlled field trials (RCFTs), assess effectiveness , or whether the intervention produces the intended effect in practice.

One important advantage of RCFTs is that secondary variables do not confound the effects of an intervention. That is, in an ideal study, an investigator wants to compare the effects of an intervention on a treatment group that is as similar as possible to the control group in all important respects except for having received the intervention. But this ideal can be affected by secondary or intervening variables—other factors by which the treatment group differs from the control group but are not of primary interest—which confound the effects of the intervention. These factors can influence the outcome of an experiment. In an RCFT, however, these secondary variables do not necessarily need to be controlled for in the design or the analysis: randomization obviates even the need to identify the secondary variables.

For many policy purposes, however, the effects of secondary variables are often critical, especially when the intervention is implemented as the result of a policy action. For this reason, the designs of RCFTs are often complex and incorporate individual differences among subjects and contextual variables so that their effects can be analyzed.

Even for the most rigorously conducted RCFTs, however, the results from one setting may not generalize to all other settings. Consequently, it may be difficult to identify “what works” in different settings from just one RCFT. Moreover, the use of RCFTs may be limited because they often require much time and expense in comparison with other approaches, or they may be precluded by ethical considerations.

Still, myriad RCFTs have been successfully conducted to inform social policy. The Digest of Social Experiments (Greenberg and Shroder, 2004) and its successor journal, Randomized Social Experiments , provide many examples.

OBSERVATIONAL STUDIES

Observational studies are nonexperimental research studies in which subjects or outcomes are observed and measured. If two groups are to be compared, the assignment of subjects among the two groups is not under the direct control of the investigator. Two types of observational studies are quasi-experiments (Campbell and Stanley, 1963) and natural experiments (see, e.g., Campbell and Ross, 1968). In the former, the investigator may manipulate the intervention; in the latter, it arises naturally. In neither type of study, however, does the investigator control which subjects receive the treatment. Observational studies can be more than passively observing data and analyzing them: for example, they may involve systematic measurement and aspects of “control,” such as manipulating the timing of an intervention to predefined although nonrandomized groups.

Because they do not involve randomization, however, observational studies may not control for the effects of secondary variables. Without experimental confirmations, the observed outcomes could be the result of any combination of a range of confounding factors. For example, subjects may be self-selected, such as students in a private school who are to be compared with students in a public school, or they may be selected by others but with different characteristics, known or unknown, that may influence the outcome of the intervention. This possible influence is called selection bias . If there is selection bias, how the intervention affects the outcome for the treatment group in comparison with the control group must be described by a model, and that model will always include some assumptions. The model may or may not help with inference for what would have happened in a randomized experiment (see National Research Council, 1998). Moreover, the assumptions underlying the model may not be widely accepted in the scientific community.

Observational studies, however, are important in revealing important associations and in guiding the formulation of theory and models. The observation of a single case can reveal unsuspected patterns and provide explanations for unmotivated forms of behavior. As put by Coburn et al. (2009, p. 1,121): “The in-depth observation made possible by the single case study

provides the opportunity to generate new hypotheses or build theory about sets of relationships that would otherwise have remained invisible.”

Observational studies also serve many other important purposes for the use of social science knowledge as evidence for public policy. The country’s wide range of longitudinal studies, for example, provides much information to guide public policy, from the extent to which people save for retirement (information provided by the Health and Retirement Study) to what different types of social welfare program benefits are actually obtained by families living in poverty (information from the Survey of Income and Program Participation). Observational studies, together with historical studies, provide the rich context in which public policy can benefit society. This use may be their most important role.

Policies are typically implemented with large and highly heterogeneous populations. Even if a policy is based on carefully designed RCFTs or other studies, implementation beyond the confines of the original study population requires careful monitoring and evaluation to make sure that the results observed in the study hold in a larger context.

A researcher must always ask if the new program is producing similar desirable outcomes in the general population as it did in the experimental setting. In the absence of a closely monitored implementation program, issues of measurement, interpretation, and purposeful or accidental deviations from a protocol inevitably creep in, with unpredictable effects on the outcome. When policies are implemented in the general population, it may be done without carefully planned designs and randomized allocation of units to treatments. Unless close monitoring of the policy occurred during implementation, it may not even be known whether the intervention as it was originally devised was what was actually implemented.

Furthermore, the ultimate goal of a policy intervention may well be something to be observed in the future, when follow-up data may be difficult to obtain. For example, although some intermediate outcomes of a program to integrate addicts into the labor force—such as the proportion of participants who are drug free and are employed after a month of treatment—can be measured more or less precisely, it is much more difficult to determine that proportion a year after treatment. Moreover, even if one is able to obtain those data, how could one determine that the results are attributable to the program and not to other factors?

Today’s trend toward accountability means that anyone proposing a new policy or intervention is also expected to prove that the intervention will “work.” Thus, thinking about credible approaches to carry out evaluation studies is almost as critical as conducting the study itself. The principles of experimental design can play an important role, even for observational evaluation.

One approach, for example, is to compare a population before and after an intervention has occurred. As long as the study includes a well-defined reference group and as long as the investigator is reasonably certain that selection bias is not important, such studies can offer some evidence of the effectiveness (or lack thereof) of an intervention. Alternatively, an evaluation study can be planned as an RCFT, in which the goal is to understand whether the original conclusions about the efficacy of the intervention hold when other factors (e.g., the target population) are not exactly the same.

Both experimental and observational studies can be used to evaluate the long-term effects of interventions. An example of such an experimental study is the work of Kellam et al. (2008) on the effect on behavioral, psychiatric, and social outcomes in young adults of a classroom behavior management program carried out when they were in first and second grades. An example of an observational study is the work of Goodman et al. (2012) on the effects of childhood physical and mental problems on adult life, based on an analysis of longitudinal data from the British National Child Development Study.

The evaluation and monitoring of an intervention as implemented is closely related to the more general concept of evolutionary learning , a process to explore how the outcome of interest responds to changes in the original intervention. Consider, for example, a new teaching method shown to be effective in a small class setting. Will it also be as effective when class sizes are large?

A critical aspect of evolutionary learning is the need to proceed in a highly controlled manner in order to understand which factor or which combination of several factors that can be varied are influencing the outcome. Alternatively, a sequence of experiments can be designed in which two or more factors are varied according to a specified plan. In the absence of carefully designed sequential learning studies, it may be difficult to untangle the effect on the outcome of each of several factors under investigation.

As in the case of evaluation and monitoring, there is a theoretical framework developed for sequential learning in studies in which the response of interest is an unknown and may be a complex function of a large

number of inputs. The approach is often known as response surface analysis: it was developed for engineering processes in the early 1950s by Box and Wilson (1951). The idea is to sequentially vary the settings of the input variables so that the response keeps improving.

Although developed for engineering processes, where it is known as evolutionary operation (Box and Draper, 1969), the approach appears to be well suited for the social sciences, in which the relationship between inputs and outputs is typically difficult to measure precisely (see the discussion in Fienberg et al., 1985). It is akin to what is referred to as a learning system that takes full advantage of each application of an intervention and extends the opportunity for discovery throughout the life-cycle of the intervention: its development, implementation, and evaluation.

META-ANALYSIS

Meta-analysis is an application of quantitative methods to combine the results of different studies (see Wachter and Straf, 1990). In such an analysis, a statistical analysis is typically made of a common numerical summary, such as an effect size, drawn from different studies (Hedges and Olkin, 1985). Today, there are many guides to conducting a meta-analysis: see, for example, Cooper (2010) and Cooper et al. (2009). Meta-analyses can lead to new hypotheses and theories and inform the design of an experiment or other research study to test them.

A major purpose of meta-analyses and other research syntheses is to reduce the uncertainty of cause-and-effect assessments of policy or program interventions. By statistically combining the results of multiple experiments, for example, the effect of a policy or program can be estimated more precisely than from any single study of an intervention. Moreover, comparing studies that are conducted with different participants in different settings allows for the examination of how different contexts affect the outcomes of a policy or program. However, if individual studies are flawed, then so will be a meta-analysis of them: thus, meta-analyses often specify standards of quality for the studies to be included.

The amalgamation of results from disparate studies can also be done with careful statistical modeling that is distinct from the approaches of meta-analysis. A good example of this approach is Toxicological Effects of Methylmercury (National Research Council, 2000b): its analysis is based on Bayesian methods developed by Dominici et al. (1999) to pool dose-

response information across a relatively large number of studies. Other examples are in Neuenschwander et al. (2010) and Turner et al. (2009).

Work on understanding how to evaluate effectiveness of a policy intervention from the total body of relevant research assembled from interdisciplinary studies has not been fully developed. An example of success, however, is researchers in early childhood intervention who have integrated knowledge about the developing brain, the human genome, molecular biology, and the interdependence of cognitive, social, and emotional development. These researchers have built a unified science-based framework for guiding priorities for early childhood policies around common concepts from neuroscience and developmental-behavioral research and broadly accepted empirical findings from four decades of program evaluation studies: see, for example, Center on the Developing Child at Harvard University (2007).

QUALITATIVE RESEARCH

In addition to experimental and observational studies, qualitative research can play important roles in developing knowledge about the societal consequences of a policy. The term covers many different types of studies, including ethnographic, historical, and other case studies; focus group interviews; content analysis of documents; interpretive sociology; and comparative and cross-national studies. The research may be derived from documentary sources, field observations, interviews with individuals or groups, and discourse between participants and researchers.

Structured, focused case comparisons are an important example of qualitative research. They are particularly useful when it is difficult to carry out studies that require high levels of control (see George, 1979; George and Bennett, 2005). By compiling and comparing case studies, it is possible to refine theory and also to develop useful assessments of the effectiveness of various types of policy interventions and the conditions that favor the effectiveness of one or another policy strategy. Structured case comparison methods have been used to inform diplomacy (Stern and Druckman, 2000) and assess policy strategies for resolving international conflicts (National Research Council, 2000a), to manage environmental resources at levels from local to global (National Research Council, 2002; Ostrom, 1990), and to evaluate efforts to engage the public in environmental decisions (Beierle and Cayford, 2002; National Research Council, 2008).

Archival studies are another example of qualitative research. They in-

volve applying a model based on past evidence or decisions to a behavior or intervention for purposes of predicting future behavior (see, e.g., Institute of Medicine, 2010). Archival data may include public data sets collected by academic institutions or government agencies, such as Supreme Court records and corporate filings, or private data sets, such as medical records collected by public or private organizations.

Qualitative research allows for a rich assessment of respondents, often unattainable in other types of studies (Institute of Medicine, 2010). Like some quantitative observational studies, they can provide the rich context in which public policy can benefit society.

THE FUTURE: NEW SOURCES OF DATA

Advances in social science and in computing technology have generated a wealth and diversity of data sources. Although privacy and proprietary concerns pose ongoing challenges for the accessibility of these sources to researchers, the data represent tremendous potential and opportunities to study social phenomena in unprecedented ways.

Federal, state, and local governments collect administrative data on populations as a by-product of program responsibilities, such as the employment history data maintained by the Social Security Administration and the personal income and wealth data maintained by the Internal Revenue Service. There are health records, school records, land-use records, and much more. A growing interest in improving and using administrative records for scientific and policy purposes has generated increased attention to the issues of privacy, data sharing, data quality, and representativeness that have been central to census and survey data for decades.

The challenges and opportunities are even more pronounced with regard to digital data. With the rise and diffusion of advanced information, communication, and computing technologies, an astounding quantity of electronic data—from demographic and geographic variables to transaction records—is amassed at an exponential rate (see Prewitt, 2010). Though much of it is commercially collected and thus proprietary, the vast reservoir of digital data increasingly includes data collected by government agencies for public use. With respect to data quality, use is constrained by the relative brevity of the time series available for variables for which collection began only recently, as well as the fact that the definitions of variables are constantly changing.

The sheer quantity and diversity of digital data with the potential for

social scientific use is astounding. As examples, consider continuous-time location data from cell phones; health data from electronic medical records and monitoring devices; consumer data from credit card transactions, online product searches and purchases, and product radio-frequency identification; satellite imagery and other forms of geocoded data; and data from social networking and other forms of social media.

The increasing “democratization of data” will enable policy analysts and policy makers to obtain much information for themselves, and it will continue to open new frontiers for social scientists. Automated information extraction and text mining have the potential to extract relevant data from the unstructured text of emails, social media posts, speeches, government reports, product reviews, and other web content. Crowd sourcing can be done through extracting information from social network websites. Longitudinal data can be compiled on millions of people with information on their locations, financial transactions, and communications. The data can be analyzed with methods of the emerging field of computational social science: network analysis, geospatial analysis, complexity models, and system dynamics, agent-based, and other social simulation models. Researchers and interested policy actors have only begun to scratch the surface of the potential of new data sources to contribute to policy making (King, 2011).

Beierle, T.C., and Cayford, J. (2002). Democracy in Practice: Public Participation in Environmental Decisions . Washington, DC: Resources for the Future.

Box, G.E.P., and Draper, N.R. (1969). Evolutionary Operation: A Statistical Method for Process Improvement . New York: Wiley.

Box, G.E.P., and Wilson, K.B. (1951). On the experimental attainment of optimum conditions (with discussion). Journal of the Royal Statistical Society, Series B , 13 (1), 1-45.

Campbell, D.T., and Ross, H.L. (1968). The Connecticut crackdown on speeding: Time series data in quasi-experimental analysis. Law and Society Review , 3 (1), 33-54.

Campbell, D.T., and Stanley, J.C. (1963). Experimental and Quasi-Experimental Designs for Research . Boston, MA: Houghton Mifflin.

Center on the Developing Child at Harvard University. (2007). A Science-Based Framework for Early Childhood Policy: Using Evidence to Improve Outcomes in Learning, Behavior, and Health for Vulnerable Children . Available: http://developingchild.harvard.edu/fles/7612/5020/4152/Policy_Framework.pdf [August 2012].

Coburn, C.E., Toure, J., and Yamashita, M. (2009). Evidence, interpretation, and persuasion: Instructional decision making at the district central office. Teachers College Record, 111 (4), 1,115-1,161.

Cooper, H.M. (2010). Research Synthesis and Meta-Analysis: A Step-by-Step Approach (fourth edition). Thousand Oaks, CA: Sage.

Cooper, H.M., Hedges, L.V., and Valentine, J.C. (Eds.). (2009). The Handbook of Research Synthesis (second edition). New York: Russell Sage Foundation.

Dominici, F., Zeger, S.L., and Samet, J.M. (1999). Combining evidence on air pollution and daily mortality from the largest 20 U.S. cities: A hierarchical modeling strategy (with discussion). Journal of the Royal Statistical Society, Series A, 163 , 263-302.

Fienberg, S.E., Singer, B., and Tanur, J. (1985). Large-scale social experimentation in the United States. In A.C. Atkinson and S.E. Fienberg (Eds.), A Celebration of Statistics: The ISI Centenary Volume (pp. 287-326). New York: Springer Verlag.

George, A.L. (1979). Case studies and theory development: The method of structured, focused comparison. In P.G. Lauren (Ed.), Diplomacy: New Approaches in History, Theory, and Policy . New York: The Free Press.

George, A.L., and Bennett, A. (2005). Case Studies and Theory Development in the Social Sciences . Cambridge, MA: MIT Press.

Goodman, A., Joyce, R., and Smith, J.P. (2012). The long shadow cast by childhood physical and mental problems on adult life. Proceedings of the National Academy of Sciences, 108 , 6,032-6,037.

Greenberg, D., and Shroder, M. (2004). The Digest of Social Experiments (third edition). Washington, DC: Urban Institute Press.

Hedges, L.V., and Olkin, I. (1985). Statistical Methods for Meta-Analysis . San Diego, CA: Academic Press.

Institute of Medicine. (2010). Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making . Committee on an Evidence Framework for Obesity Prevention Decision Making, S.K. Kumanyika, L. Parker, and L.J. Sim, Eds. Food and Nutrition Board. Washington, DC: The National Academies Press.

Kellam, S.G., Reid, J., and Balster, R.L. (2008). Effects of a universal classroom behavior program in first and second grades on young adult problem outcomes, Drug and Alcohol Dependence, 95 , S1-S4.

King, G. (2011). Ensuring the data-rich future of the social sciences. Science, 331 (6,018), 719-721.

National Research Council. (1998). Assessing Evaluation Studies: The Case of Bilingual Education Strategies . Panel to Review Evaluation Studies of Bilingual Education, M.M. Meyer and S.E. Fienberg, Eds. Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council. (2000a). International Conflict Resolution After the Cold War. Committee on International Conflict Resolution, P.C. Stern and D. Druckman, Eds. Washington, DC: National Academy Press.

National Research Council. (2000b). Toxicological Effects of Methylmercury . Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology, Commission on Life Sciences. Washington, DC: National Academy Press.

National Research Council. (2002). The Drama of the Commons. Committee on the Human Dimensions of Global Change, E. Ostrom, T. Dietz, N. Dolsak, P.C. Stern, S. Stonich, and E.U. Weber, Eds. Committee on the Human Dimensions of Global Change, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2008). Public Participation in Environmental Assessment and Decision Making . Panel on Public Participation in Environmental Assessment and Decision Making, T. Dietz and P.C. Stern, Eds. Committee on the Human Dimensions of Global Change, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Neuenschwander, B., Capkun-Niggli, G., Branson, M., and Spiegelhalter, D.J. (2010). Summarizing historical information on controls in clinical trials. Clinical Trials, 7 (1), 5-18.

Ostrom, E. (1990). Governing the Commons: The Evolution of Institutions for Collective Action . New York: Cambridge University Press.

Prewitt, K. (2010). Science starts not after measurement but with measurement. The Annals of the American Academy of Political and Social Sciences, 631 (1), 7-13.

Stern, P.C., and Druckman, D. (2000). Evaluating interventions in history: The case of international conflict resolution. International Studies Review, 2 (1), 33-63.

Turner, R.M., Spiegelhalter, D.J., Smith, G.C.S., and Thompson, S.G. (2009). Bias modelling in evidence synthesis. Journal of the Royal Statistical Society, Series A, 172 , 23-47.

Wachter, K.W., and Straf, M.L. (1990). The Future of Meta-Analysis . New York: Russell Sage Foundation.

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Using Science as Evidence in Public Policy encourages scientists to think differently about the use of scientific evidence in policy making. This report investigates why scientific evidence is important to policy making and argues that an extensive body of research on knowledge utilization has not led to any widely accepted explanation of what it means to use science in public policy. Using Science as Evidence in Public Policy identifies the gaps in our understanding and develops a framework for a new field of research to fill those gaps.

For social scientists in a number of specialized fields, whether established scholars or Ph.D. students, Using Science as Evidence in Public Policy shows how to bring their expertise to bear on the study of using science to inform public policy. More generally, this report will be of special interest to scientists who want to see their research used in policy making, offering guidance on what is required beyond producing quality research, beyond translating results into more understandable terms, and beyond brokering the results through intermediaries, such as think tanks, lobbyists, and advocacy groups. For administrators and faculty in public policy programs and schools, Using Science as Evidence in Public Policy identifies critical elements of instruction that will better equip graduates to promote the use of science in policy making.

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Home Market Research

Social Research – Definition, Types and Methods

Social Research: Definition

Social Research is a method used by social scientists and researchers to learn about people and societies so that they can design products/services that cater to various needs of the people. Different socio-economic groups belonging to different parts of a county think differently. Various aspects of human behavior need to be addressed to understand their thoughts and feedback about the social world, which can be done using Social Research. Any topic can trigger social research – new feature, new market trend or an upgrade in old technology.

Select your respondents

Social Research is conducted by following a systematic plan of action which includes qualitative and quantitative observation methods.

Qualitative methods rely on direct communication with members of a market, observation, text analysis. The results of this method are focused more on being accurate rather than generalizing to the entire population.
Quantitative methods use statistical analysis techniques to evaluate data collected via surveys, polls or questionnaires.

LEARN ABOUT: Research Process Steps

Social Research contains elements of both these methods to analyze a range of social occurrences such as an investigation of historical sites, census of the country, detailed analysis of research conducted to understand reasons for increased reports of molestation in the country etc.

A survey to monitor happiness in a respondent population is one of the most widely used applications of social research. The happiness survey template can be used by researchers an organizations to gauge how happy a respondent is and the things that can be done to increase happiness in that respondent.

Learn more: Public Library Survey Questions + Sample Questionnaire Template

Types of Social Research

There are four main types of Social Research: Qualitative and Quantitative Research, Primary and Secondary Research.

Qualitative Research: Qualitative Research is defined as a method to collect data via open-ended and conversational discussions, There are five main qualitative research methods- ethnographic research, focus groups, one-on-one online interview, content analysis and case study research. Usually, participants are not taken out of their ecosystem for qualitative data collection to gather information in real-time which helps in building trust. Researchers depend on multiple methods to gather qualitative data for complex issues.

Quantitative Research: Quantitative Research is an extremely informative source of data collection conducted via mediums such as surveys, polls, and questionnaires. The gathered data can be analyzed to conclude numerical or statistical results. There are four distinct quantitative research methods: survey research , correlational research , causal research and experimental research . This research is carried out on a sample that is representative of the target market usually using close-ended questions and data is presented in tables, charts, graphs etc.

For example, A survey can be conducted to understand Climate change awareness among the general population. Such a survey will give in-depth information about people’s perception about climate change and also the behaviors that impact positive behavior. Such a questionnaire will enable the researcher to understand what needs to be done to create more awareness among the public.

Learn More: Climate Change Awareness Survey Template

Primary Research: Primary Research is conducted by the researchers themselves. There are a list of questions that a researcher intends to ask which need to be customized according to the target market. These questions are sent to the respondents via surveys, polls or questionnaires so that analyzing them becomes convenient for the researcher. Since data is collected first-hand, it’s highly accurate according to the requirement of research.

For example: There are tens of thousands of deaths and injuries related to gun violence in the United States. We keep hearing about people carrying weapons attacking general public in the news. There is quite a debate in the American public as to understand if possession of guns is the cause to this. Institutions related to public health or governmental organizations are carrying out studies to find the cause. A lot of policies are also influenced by the opinion of the general population and gun control policies are no different. Hence a gun control questionnaire can be carried out to gather data to understand what people think about gun violence, gun control, factors and effects of possession of firearms. Such a survey can help these institutions to make valid reforms on the basis of the data gathered.

Learn more: Wi-Fi Security Survey Questions + Sample Questionnaire Template

Secondary Research: Secondary Research is a method where information has already been collected by research organizations or marketers. Newspapers, online communities, reports, audio-visual evidence etc. fall under the category of secondary data. After identifying the topic of research and research sources, a researcher can collect existing information available from the noted sources. They can then combine all the information to compare and analyze it to derive conclusions.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Social Research Methods

Surveys: A survey is conducted by sending a set of pre-decided questions to a sample of individuals from a target market. This will lead to a collection of information and feedback from individuals that belong to various backgrounds, ethnicities, age-groups etc. Surveys can be conducted via online and offline mediums. Due to the improvement in technological mediums and their reach, online mediums have flourished and there is an increase in the number of people depending on online survey software to conduct regular surveys and polls.

There are various types of social research surveys: Longitudinal , Cross-sectional , Correlational Research . Longitudinal and Cross-sectional social research surveys are observational methods while Correlational is a non-experimental research method. Longitudinal social research surveys are conducted with the same sample over a course of time while Cross-sectional surveys are conducted with different samples.

For example: It has been observed in recent times, that there is an increase in the number of divorces, or failed relationships. The number of couples visiting marriage counselors or psychiatrists is increasing. Sometimes it gets tricky to understand what is the cause for a relationship falling apart. A screening process to understand an overview of the relationship can be an easy method. A marriage counselor can use a relationship survey to understand the chemistry in a relationship, the factors that influence the health of a relationship, the challenges faced in a relationship and expectations in a relationship. Such a survey can be very useful to deduce various findings in a patient and treatment can be done accordingly.

Another example for the use of surveys can be to gather information on the awareness of disasters and disaster management programs. A lot of institutions like the UN or the local disaster management team try to keep their communities prepared for disasters. Possessing knowledge about this is crucial in disaster prone areas and is a good type of knowledge that can help everyone. In such a case, a survey can enable these institutions to understand what are the areas that can be promoted more and what regions need what kind of training. Hence a disaster management survey can be conducted to understand public’s knowledge about the impact of disasters on communities, and the measures they undertake to respond to disasters and how can the risk be reduced.

Learn more: NBA Survey Questions + Sample Questionnaire Template

Experiments: An experimental research is conducted by researchers to observe the change in one variable on another, i.e. to establish the cause and effects of a variable. In experiments, there is a theory which needs to be proved or disproved by careful observation and analysis. An efficient experiment will be successful in building a cause-effect relationship while proving, rejecting or disproving a theory. Laboratory and field experiments are preferred by researchers.

Interviews: The technique of garnering opinions and feedback by asking selected questions face-to-face, via telephone or online mediums is called interview research. There are formal and informal interviews – formal interviews are the ones which are organized by the researcher with structured open-ended and closed-ended questions and format while informal interviews are the ones which are more of conversations with the participants and are extremely flexible to collect as much information as possible.

LEARN ABOUT: 12 Best Tools for Researchers

Examples of interviews in social research are sociological studies that are conducted to understand how religious people are. To this effect, a Church survey can be used by a pastor or priest to understand from the laity the reasons they attend Church and if it meets their spiritual needs.

Observation: In observational research , a researcher is expected to be involved in the daily life of all the participants to understand their routine, their decision-making skills, their capability to handle pressure and their overall likes and dislikes. These factors and recorded and careful observations are made to decide factors such as whether a change in law will impact their lifestyle or whether a new feature will be accepted by individuals.

Learn more:

Quantitative Observation

Qualitative Observation

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Organizing Your Social Sciences Research Paper: Types of Research Designs

Purpose of Guide
Writing a Research Proposal
Design Flaws to Avoid
Independent and Dependent Variables
Narrowing a Topic Idea
Broadening a Topic Idea
The Research Problem/Question
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
The C.A.R.S. Model
Background Information
Theoretical Framework
Citation Tracking
Evaluating Sources
Reading Research Effectively
Primary Sources
Secondary Sources
What Is Scholarly vs. Popular?
Is it Peer-Reviewed?
Qualitative Methods
Quantitative Methods
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism [linked guide]
Annotated Bibliography
Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
Review and synthesize previously published literature associated with the research problem,
Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
Replication is possible.
There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
Either original data or secondary data can be used in this design.
In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
Studies cannot be utilized to establish cause and effect relationships.
This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
If the limitations are understood, they can be a useful tool in developing a more focused study.
Descriptive studies can yield rich data that lead to important recommendations in practice.
Appoach collects a large amount of data for detailed analysis.
The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
Approach provides the highest level of evidence for single studies.
The design is artificial, and results may not generalize well to the real world.
The artificial settings of experiments may alter the behaviors or responses of participants.
Experimental designs can be costly if special equipment or facilities are needed.
Some research problems cannot be studied using an experiment because of ethical or technical reasons.
Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

Familiarity with basic details, settings, and concerns.
Well grounded picture of the situation being developed.
Generation of new ideas and assumptions.
Development of tentative theories or hypotheses.
Determination about whether a study is feasible in the future.
Issues get refined for more systematic investigation and formulation of new research questions.
Direction for future research and techniques get developed.
Design is a useful approach for gaining background information on a particular topic.
Exploratory research is flexible and can address research questions of all types (what, why, how).
Provides an opportunity to define new terms and clarify existing concepts.
Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

The historical research design is unobtrusive; the act of research does not affect the results of the study.
The historical approach is well suited for trend analysis.
Historical records can add important contextual background required to more fully understand and interpret a research problem.
There is often no possibility of researcher-subject interaction that could affect the findings.
Historical sources can be used over and over to study different research problems or to replicate a previous study.
The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
Interpreting historical sources can be very time consuming.
The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58; Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
The data collection method may change over time.
Maintaining the integrity of the original sample can be difficult over an extended period of time.
It can be difficult to show more than one variable at a time.
This design often needs qualitative research data to explain fluctuations in the results.
A longitudinal research design assumes present trends will continue unchanged.
It can take a long period of time to gather results.
There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
The strengths of one method can be used to overcome the inherent weaknesses of another method.
Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
The researcher is able to collect in-depth information about a particular behavior.
Can reveal interrelationships among multifaceted dimensions of group interactions.
You can generalize your results to real life situations.
Observational research is useful for discovering what variables may be important before applying other methods like experiments.
Observation research designs account for the complexity of group behaviors.
Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
There can be problems with bias as the researcher may only "see what they want to see."
There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
Sources or subjects may not all be equally credible.
Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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Research approaches in the humanities, social sciences, or sciences.

Social Sciences

Researching in the Social Sciences

Social scientists interpret and analyze human behavior, generally using empirical methods of research. Though original data gathering and analysis are central to social sciences research, researchers also use library and Web sources to--

obtain raw data for model building or analysis
locate information about a particular model, theory, or methodology to be used in a research project
review the literature to place new research in context

Subjects of study in the social sciences are often interdisciplinary, so your searching will likely need to be, as well. A review of the literature for a social sciences research project not only should identify what research has been done but also compare and contrast the available information and evaluate its significance.

Each of the social sciences has a well-developed set of research tools to help you find relevant material. Some of the WashU Libraries Research Guides listed below may give you ideas for beginning your research. You should also consult your subject librarian for help getting started or refining your search.

Find a Subject Librarian This is a list of the Subject Librarians by academic subject.

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Types of Sources

Types of sources

Primary sources are original material, created at the time of the event or by the subject you are studying. They may include statistics, survey and poll data, field notes, transcripts, photographs, and many other examples. This kind of material is the closest you can get to your actual subject, raw and unfiltered by later scholars and critics.

Secondary sources are works that analyze primary sources or other secondary sources. These include journal articles, monographs about a subject or person, and critical reviews. All of these can also act as primary sources, depending upon your subject of research.

Tertiary sources index or otherwise collect primary and secondary sources. Examples are encyclopedias, bibliographies, dictionaries, and online indicies. These sources tend to be most useful as jumping off points for your research, leading you to the more in-depth secondary and primary material that you will need to conduct a thorough study.

Conducting the Literature Review

The literature review is an important part of researching in the social sciences. Research and the literature review in particular are cyclical processes.

Where do I Start?
Where Should I Look?
How Do I Know I am Done?
How Do I Organize My Literature Review?

Where do I start? The Research Question Begin with what you know: What are the parameters of your research area? Do you have any particular interests in a relevant topic? Has something you've read or talked about in a class caught your attention? Brainstorm some keywords you know are related to your topic, and start searching. Do a search in a few of the Search Resources boxes on the Libraries' Website and see what comes up. Scan titles. Do a Google Scholar search. Read an encyclopedia article. Get as much background information as you can, taking note of the most important people, places, ideas, events. As you read, take notes-- these will be the building blocks of your future searches. It's probable your question will change over the course of your reading and research. No worries! If you're unsure about your topic, check with your faculty mentor.

Some tips Throw out a wide net and read, read, read. Consider the number and kinds of sources you'll need. Which citation style should you use? What time period should it cover? Is currency important? What do you need to be aware of related to scholarly versus popular materials?

Read widely but selectively.
Follow the citation trail -- building on previous research by reviewing bibliographies of articles and books that are close to your interest.
Synthesize previous research on the topic.
Aim to include both summary and synthesis.
Focus on ways to have the body of literature tell its own story. Do not add your own interpretations at this point.
Look for patterns and find ways of tying the pieces together.

Where should I look?

Databases, journals, books
Review articles
Organizations

How do I know I am done? One key factor in knowing you are done is that you keep running into the same articles and materials. With no new information being uncovered you can assume you've exhausted your current search and should modify search terms, or perhaps you have reached a point of exhaustion with the available research.

How do I organize my literature review?

Identify the organizational structure you want to use: chronologically, thematically, or methodologically.
Start writing: let the literature tell the story, find the best examples, summarize instead of quote, synthesize by rephrasing (but cite!) in context of your work.

Additional information available @ The Literature Review: A Few Tips on Conducting It (University of Toronto)

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USC Libraries
Research Guides

Organizing Your Social Sciences Research Paper

6. The Methodology
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I. Groups of Research Methods

There are two main groups of research methods in the social sciences:

The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II. Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
Tools and methods used to identify and collect information, and how you identified relevant variables,
The ways in which you processed the data and the procedures you used to analyze that data, and
The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE: Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE: If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III. Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Sampling is the statistical process of selecting a subset—called a ‘sample’—of a population of interest for the purpose of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviours within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalised back to the population of interest. Improper and biased sampling is the primary reason for the often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every US Presidential election.

The sampling process

As Figure 8.1 shows, the sampling process comprises of several stages. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organisation, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meet certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyse the behaviour of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behaviour over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population—usually a list with contact information—from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organisations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalisable to the population. For instance, if your target population is organisational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalisable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganisation and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms, most of which are medium or small sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P LargeCap, MidCap, or SmallCap lists, but includes publicly traded firms (and not private firms) and is hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to examine the success rate of a new ‘quit smoking’ program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular ‘quit smoking’ program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalisability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: every unit in the population has a known non-zero probability of being sampled, and the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called ‘strata’), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1,000 firms, you can start by categorising the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favour of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of the sample within each subgroup does not reflect the proportions in the sampling frame—or the population of interest—and the smaller subgroup (large-sized firms) is oversampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the ‘large’ group, 60 from the ‘medium’ group and 120 from the ‘small’ group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach are weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into ‘clusters’—usually along geographic boundaries—randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every office in those counties. However, depending on between-cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalisable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorise a sampling frame of firms into ‘high profitable’ firms and ‘low profitable firms’ based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Matched-pairs sampling techniques are often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools. Within each school, you can select a simple random sample of grade levels, and within each grade level, you can select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-probability sampling

Non-probability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, non-probability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalised back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping centre and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centres. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping centre such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping centre close to a university will attract primarily university students with unique purchasing habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalisability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalisable inferences.

Quota sampling. In this technique, the population is segmented into mutually exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70 per cent Caucasians, 15 per cent Hispanic-Americans, and 13 per cent African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping centre and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

Non-proportional quota sampling is less restrictive in that you do not have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African-Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping centre in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population, but may be useful in that it allows capturing the opinions of small and under-represented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample a group of corporate accountants who are familiar with this Act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalisable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also work in network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a ‘statistic’ is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not ‘statistic’ because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population. The difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to the corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical—and equal to the population parameter—and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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By Haruto Nakamura
Last Updated August 18, 2024
Lifestyle & Culture

Humanities Versus Social Science: Key Differences Explained

When deciding between the Humanities versus Social Science, it’s essential to understand their core differences. The humanities focus on human culture and intellectual expression, exploring areas like literature, philosophy, and history. Social sciences study human society and social relationships, involving disciplines such as sociology, psychology, and economics. This article will help you grasp these differences and guide you in making an informed choice about your educational and career path.

Key takeaways.

Humanities and social sciences differ in focus: humanities emphasize cultural and theoretical explorations, while social sciences analyze societal behaviors and interactions through empirical methods.

Both fields provide distinct career opportunities, with humanities graduates often pursuing roles in education and cultural preservation, and social science graduates typically entering data analysis and policy development sectors.

Interdisciplinary studies that combine elements from both humanities and social sciences can enhance critical thinking and adaptive skills, offering a well-rounded perspective on human behavior and societal dynamics.

Understanding Humanities

An illustration depicting the concept of humanities, showcasing various aspects of human culture and society.

Humanities encompass a wide array of disciplines that study various aspects of human culture and society, including the humanities discipline. This includes fields such as:

anthropology

archaeology

linguistics

The focus is on understanding human beings’ creative and intellectual expressions, offering a deeper understanding of human experiences and cultural contexts.

Examining these humanities disciplines offers insights into the human condition and human interactions within different environments.

Approach in Humanities

The study of humanities often revolves around qualitative methodologies that delve into the meanings and contexts of cultural artifacts and human experiences. Unlike the scientific method used in natural sciences, humanities research emphasizes theoretical, abstract, and conceptual perspectives. This approach allows researchers to explore the depths of human culture, creativity, and philosophical expressions, providing a nuanced understanding of human societies and behaviors.

Humanities scholars use this qualitative lens to interpret and explain the intricacies of human culture and behavior. They gain insights by analyzing texts, artworks, and historical records, thereby contributing significantly to our broader understanding of the human experience. This method fosters critical thinking and analytical skills, essential for anyone seeking to comprehend the intricacies of human society.

Key Areas of Study in Humanities

The humanities subjects cover a vast range of subjects, each offering unique perspectives on human culture and society. Key areas of study include:

Philosophy, which tackles fundamental questions about existence, knowledge, and ethics

History, which examines past events and their impact on the present and future

Literature, which explores human experiences through written works

Language arts, which focus on the structure and function of language itself

Other notable areas are the performing arts, such as drama and music, and the visual arts, including disciplines like art history and fine arts. Gender studies, regional studies, and religious studies also play vital roles in understanding the diverse aspects of human societies and cultures. These areas offer a robust framework for understanding how humans express and interpret their world.

Career Paths for Humanities Graduates

Humanities graduates possess versatile skills that open doors to diverse career paths. Common career options include:

Roles in education, where they can teach subjects related to their field of study

Cultural heritage organizations, like museums and galleries

These options offer opportunities for humanities graduates to apply their skills in various professional settings.

There are also ample opportunities in non-profit organizations and community development, where humanities graduates can apply their understanding of human culture and societies to foster social change. Public relations, cultural preservation, and arts administration are other career paths that benefit from the critical thinking, communication skills, and cultural awareness gained through humanities studies.

Exploring Social Science

Social sciences, often referred to as “soft sciences,” social science focuses on the study of human society and social relationships. The goal is to understand societal structures and human interactions, ultimately aiming to solve societal problems. These disciplines examine various aspects of human behavior and societal dynamics, providing empirical insights that help address real-world issues.

Methodologies in Social Sciences

Social scientists employ both qualitative and quantitative research methods. This combination helps them effectively study various aspects of society. Quantitative methods focus on collecting and analyzing numerical data to identify patterns and relationships in social phenomena. This approach allows researchers to test hypotheses and validate findings using empirical evidence.

On the other hand, qualitative methods in social science focus on understanding the deeper meanings and contexts of human behavior. Techniques such as interviews, case studies, and ethnography enable social scientists to gain a comprehensive understanding of social issues from multiple perspectives. This combination ensures a comprehensive analysis of human societies and interactions.

Core Disciplines in Social Science

Social sciences include several core disciplines, each with a unique focus and methodology. Sociology, for instance, examines the structures and dynamics of societies, exploring how social institutions and relationships influence human behavior. Psychology delves into the emotions, thoughts, and behaviors of individuals, providing insights into mental processes and interpersonal interactions within social science disciplines.

Other key disciplines include economics, which analyzes the production, distribution, and consumption of goods and services, and political science, which studies governance systems, political activities, and public policies. Anthropology investigates human societies and cultures from a holistic perspective, incorporating elements from both humanities and social sciences.

Together, these fields contribute to a broader understanding of societal dynamics and human behavior.

Career Opportunities in Social Science

A degree in social sciences opens up a wide range of career opportunities. Some examples include:

Market research analysts, who use social science research methods to understand consumer behavior and market trends

Social workers, who apply their knowledge of human behavior and social institutions to help individuals and communities

Community service managers, who oversee social service programs and lead community organizations

This diverse field allows for various paths depending on your interests and skills.

Government and non-profit organizations also offer roles where social science graduates can influence public policy and drive social change. Positions such as political consultants, urban planners, and sociologists also benefit from the analytical and empirical skills developed through social science studies.

For those interested in academic or advanced research careers, pursuing a master’s or doctoral degree is often necessary.

Comparative Analysis: Humanities vs Social Science

An illustration depicting the comparative analysis of humanities versus social science, highlighting their unique characteristics.

Understanding the distinctions and overlaps between humanities and social sciences is essential for students making an informed decision about their field of study. Both humanities and social sciences explore human behavior and societies, but through different lenses and methodologies.

This comparative analysis will highlight the key differences and intersections between these two disciplines.

Nature and Focus

The humanities mainly concentrate on exploring human culture with a humanities focus. This includes subjects such as politics, history, philosophy, art, and literature. This field emphasizes theoretical exploration and creative expression, fostering skills such as critical thinking and effective communication.

In contrast, social sciences concentrate on analyzing human interaction and societal behaviors, using empirical methods to study branches like sociology, psychology, and economics. While humanities graduates often engage in careers that involve interpreting and preserving cultural artifacts, social science graduates typically work in roles that require data analysis and practical applications of social theories.

Despite these differences, both fields contribute valuable insights into the complexities of human societies and behaviors, often intersecting in areas like cultural studies and political science.

Research Methods

One of the main differences between humanities and social sciences lies in their research methods. Humanities research mainly employs qualitative methods like analytical, critical, or speculative approaches that focus on meaning rather than empirical data. This method allows for an in-depth exploration of cultural and philosophical concepts, fostering a rich understanding of human experiences.

In contrast, social sciences often rely on empirical methods and data-driven research to study societal behaviors and interactions. Quantitative methods, such as surveys and statistical analysis, provide objective insights that can be tested and validated.

Combining qualitative and quantitative approaches ensures a thorough analysis of social phenomena, blending theoretical and empirical perspectives.

Practical Applications

The practical applications of degrees in humanities and social sciences vary significantly, reflecting the distinctive nature of each field. Humanities graduates often find employment in management and professional occupations, such as cultural preservation, education, and public relations. Although the initial income for humanities graduates may be lower compared to other fields, obtaining advanced or professional degrees can significantly boost their earning potential.

On the other hand, social science graduates typically pursue careers that involve data analysis, policy development, and social services. Roles such as market research analyst, political consultant, and social worker require the empirical and analytical skills developed through social science research.

Both fields offer diverse career opportunities, with the choice ultimately depending on individual interests and goals.

Choosing Between Humanities and Social Science

Choosing between humanities and social science can be challenging, but understanding the key differences and skills developed in each field can guide this decision. Students should consider their personal interests, career aspirations, and the types of questions that intrigue them when making their choice.

The following insights into the skills cultivated in each field and degree considerations can help influence this decision.

Skill Sets Developed

Humanities endow students with valuable skills, while social sciences prepare them for various professional environments. Humanities students develop critical thinking, analytical skills, and effective communication through their studies. These skills are crucial for careers in education, cultural preservation, and public relations, where interpreting and conveying complex ideas is essential.

In contrast, social science students gain analytical and empirical skills that are highly sought after in roles such as market research, policy development, and social services. The interdisciplinary approach of social sciences also enhances creative problem-solving abilities, making graduates adaptable to diverse career paths.

Both fields cultivate a deep understanding of human behavior and societal dynamics, preparing students for diverse professional opportunities.

Degree Considerations

When deciding between a degree in humanities or social science, students should consider their interests and career goals. A humanities degree explores topics historically or theoretically, focusing on the abstract aspects of human culture. This degree is ideal for those interested in critical thinking, creative expression, and understanding cultural contexts.

On the other hand, a social science degree emphasizes the study of societal patterns and structures, using empirical methods to analyze human interactions. This field is suitable for students who are interested in practical applications of social theories and data-driven research.

After completing a bachelor’s degree, students can pursue advanced degrees in related disciplines or professional fields such as law, business, or social work.

Learn more, Humanities vs Social Science. Which Degree to Study

Interdisciplinary Opportunities

Interdisciplinary studies offer a unique opportunity to combine insights from both humanities and social sciences, providing a well-rounded perspective on human behavior and societal dynamics. Many academic programs encourage interdisciplinary approaches, allowing students to explore overlapping fields and benefit from diverse methodologies.

This section will delve into the overlapping fields and the benefits of pursuing interdisciplinary studies.

Overlapping Fields

Fields like anthropology and history incorporate elements from both humanities and social sciences. These fields offer a holistic approach to studying human societies, combining qualitative and empirical methods to gain a comprehensive understanding of cultural and social phenomena. Gender studies also bridges the gap between humanities and social sciences, examining issues of gender and identity from multiple perspectives.

By exploring these overlapping fields, students can develop a multifaceted view of human behavior and societal dynamics. This approach encourages collaboration and enriches both fields, offering a broader framework for addressing complex social issues. It also allows students to tailor their education to their specific interests, combining insights from various disciplines.

Benefits of Interdisciplinary Studies

Pursuing interdisciplinary studies in humanities and social sciences offers manifold benefits. This approach enhances critical thinking and communication skills, which are highly valued by employers across different sectors. Experiential learning opportunities like internships, research projects, and study abroad programs provide practical experience and deepen students’ understanding of diverse cultures, including aspects of international relations.

Moreover, interdisciplinary studies equip graduates with adaptable skills that enable them to thrive in various career paths, from writing and public relations to law and education. Drawing on multiple fields of knowledge fosters innovative solutions to social problems and prepares students for leadership roles in their chosen professions. This flexibility makes interdisciplinary studies an attractive option for those seeking a dynamic and comprehensive educational experience.

As we draw to a close, it’s clear that both humanities and social sciences offer rich, diverse pathways to understanding human behavior and society. Humanities focus on the cultural, artistic, and philosophical aspects of human life, fostering critical thinking and creativity. In contrast, social sciences provide empirical insights into societal structures and interactions, emphasizing data analysis and practical applications. By considering your interests, career aspirations, and the skills you wish to develop, you can make an informed choice between these two fields. Remember, interdisciplinary studies also offer a compelling option, blending the strengths of both disciplines to provide a well-rounded educational experience.

Frequently Asked Questions

What are the main differences between humanities and social sciences.

The main difference between humanities and social sciences lies in their focus and methodology; humanities emphasize qualitative insights into human culture and values, while social sciences employ empirical research and quantitative analysis to study societal behaviors and structures. Each discipline thus offers unique perspectives on the human experience.

What career opportunities are available for humanities graduates?

Humanities graduates have a diverse range of career opportunities, including roles in education, publishing, journalism, and cultural organizations. These fields allow for meaningful contributions to society while leveraging the skills developed during their studies.

What methodologies are used in social sciences?

Social sciences employ both qualitative and quantitative methodologies, such as case studies, interviews, surveys, and statistical analysis, to explore societal behaviors and interactions. These diverse methods facilitate a comprehensive understanding of complex social dynamics.

How do interdisciplinary studies benefit students?

Interdisciplinary studies significantly benefit students by enhancing critical thinking and communication skills while providing experiential learning opportunities. This approach equips graduates with adaptable skills essential for diverse career paths.

What should students consider when choosing between humanities and social sciences?

When choosing between humanities and social sciences, students should evaluate their interests, career aspirations, and the skills they wish to acquire. Additionally, considering the practical applications of each field can guide their decision effectively.

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The 6-Day International Workshop on Research Methods in Social Sciences organised by the School of Social Sciences and Languages in association with SPARC-Govt of India is a comprehensive event designed to enhance the research capabilities of research scholars from the broad disciplines of Social Sciences and Commerce backgrounds, held from August 26th to August 31st, 2024. This workshop will bring together renowned experts and researchers with an aim to provide participants with a thorough understanding of contemporary research methods, equipping them with the skills necessary to conduct rigorous and impactful research. Each workshop day is focused on qualitative and quantitative research designs, econometrics, data collection techniques, data analysis methods, and the ethical considerations involved in conducting research. The sessions deal with hands-on training and group discussions, allowing research scholars to actively engage with the material and apply what they learned to real-world scenarios. Hands-on sessions in R, SPSS, Strata, Atlas.ti will be dealt with, enabling participants an opportunity to discuss and refine their methodologies under the guidance of resource persons. This interactive format will not only enhance their understanding but also foster collaboration among participating researchers. The workshop will conclude with a panel discussion on the future of Social Sciences research, where experts will emphasize the importance of innovative approaches and interdisciplinary research. Participants will leave the workshop with a deeper knowledge of research methods, new project ideas, and connections with fellow researchers that will last well beyond the event. The success of this workshop will underscore the ongoing need for such international collaborations in advancing research in the Social Sciences areas.

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Romancing the stone: DMSE researchers crack magnetic garnet mystery

Scientists love a good mystery—it keeps them querying, testing, changing variables, and trying again. Sometimes a mystery lingers for decades, outlasting technological limitations—and setting the stage for a scientific breakthrough. Such was the challenge Allison Kaczmarek and her colleagues faced four years ago. A graduate student in MIT’s Department of Materials Science and Engineering (DMSE), Kaczmarek set out to uncover why the magnetic properties of garnet crystal thin films were so peculiar. Artificial garnets, synthesized in labs by scientists, are similar to the gemstone but are made with iron instead of silicon, making them magnetic. Typically, these crystals are isotropic, meaning their magnetic behavior doesn’t vary much no matter which way they’re positioned. But when the same magnetic garnets were grown as thin films for a now obsolete technology called bubble memory, their magnetic properties varied dramatically in different directions. This variation in thin films presented a compelling puzzle for researchers to solve. Thermodynamics pioneer Herbert Callen attempted to unravel the phenomenon, known as growth-induced anisotropy, in 1971. He theorized that atoms of elements added during the growth process arranged themselves in a certain way, prompting the unusual properties. But at the time, the technology needed to observe what was happening at the atomic level didn’t yet exist, so Callen’s theory remained just that. “Researchers didn’t really have a way to explain it other than this unproven theory,” Kaczmarek said. “So for a long time, they were just like, ‘Yeah, it must be that the atoms are ordering.’” Until now. After more than 50 years, Kaczmarek and her advisors, Professors Geoffrey Beach and Caroline Ross, have confirmed Callen’s theory, in a recent Nature Communications paper . Using advanced imaging and analysis techniques, the researchers showed that in artificial garnet films made from rare-earth elements europium and thulium, the europium and thulium atoms arranged themselves in patterns that cause directionally dependent magnetism. The findings suggest that by controlling the arrangement of atoms in these materials, researchers can fine-tune their properties, which could lead to the development of magnetic devices such as superfast memory technologies. These advancements could impact fields such as data storage, advanced electronics, and medical imaging. Other authors of the paper include DMSE alum Ethan Rosenberg; graduate students Yixuan Song, Kevin Ye, and Gavin Winter; research specialist Aubrey Penn; and Associate Professor Rafael Gómez-Bombarelli.

The making of a mystery

The quest to explain the unusual magnetic properties of garnet thin films began with Rosenberg, who started growing these films before finishing his PhD in 2021, when Kaczmarek was a first-year student. Though he couldn’t fully explore them, his efforts set the stage for future discoveries. “He had this crazy idea, ‘Oh, maybe we should try to look directly at ordering in these materials,” Kaczmarek said. “Now I’m in my fourth year and it’s just wrapping up. So it’s been a long journey.” Rosenberg, and then Kaczmarek, started by revisiting research from the 1970s, when materials researchers made films of artificial garnet crystals for bubble memory, a data storage technology. Thin films are layers of material deposited onto a substrate, usually a flat surface made of glass or another material. Researchers make them to study and optimize their properties, enabling advancements in various technologies. Bubble memory relied on thin films of magnetic material to hold “bubbles,” which stored information. “They’d have these chips with the garnet material inside, and they’d have ways of creating these bubbles, which are magnetic domains, and moving them around—that’s how they stored data,” explained Ross, the Ford Professor of Engineering in DMSE. Since the bubbles needed to be stable as they were moved around to read, write, or store data, their magnetization had to point along particular directions, making lab-grown garnet films ideal for the task. The thin-film production process is key to the material’s unusual properties. Researchers have been making magnetic garnets since the mid-20th century for microwave technology, used in radar systems and communication devices, and for magneto-optical devices in sensors and laser technology. But unlike their thin-film counterparts, the magnetism in three-dimensional garnet crystals does not have a preferred direction. At the time, the methods for growing thin films differed from today’s sophisticated deposition processes, which involve applying nanometer-thin layers of material to a surface in a vacuum chamber. To make garnet thin films, researchers used to heat raw materials like rare-earth and iron oxides until they melted and then let them grow on a garnet substrate. Two researchers making garnet thin films, Allan Rosencwaig and W. J. Tabor of Bell Laboratories, noticed that atoms of various elements they used had integrated into the material’s crystal structure—the repeating arrangement of atoms in gemstones, metals, and ceramics. So they consulted with Callen, to understand the phenomenon better. Callen was renowned for his expertise in statistical mechanics and thermodynamics, key fields for understanding complex atomic-level phenomena. Callen proposed that the specific conditions during the thin-film growth process—temperature, composition, and the surface they were grown on—led to the atoms arranging themselves in specific patterns. He pointed out that as the atoms joined the garnet material, the “sites,” or locations where they attach, have different shapes. For example, some sites might be triangular while others look square. Callen theorized that atoms of different elements prefer specific sites in the crystal structure, and this selective arrangement is what makes the material’s magnetism directionally dependent. This atomic ordering was less pronounced in three-dimensional garnet crystals because the atoms attached to various sites in the crystal structure at once, so they didn’t arrange themselves in the same distinct patterns. “That’s how this theory that there’s ordering started, but there hasn’t been real observation of the order until now,” Kaczmarek said.

Piecing it together

That’s partly because researchers spent less time growing and thinking about garnet thin films. Bubble memory fell out of favor in the late ’70s, as hard disk and solid-state drives got faster and their storage capacity increased. Despite the waning interest in studying the material’s properties for bubble memory, Kaczmarek saw an untapped opportunity to revisit and solve the longstanding puzzle. Without a deeper understanding of the properties, researchers could miss out on discovering advanced magnetic materials for countless new applications. She sought to validate Callen’s theory, re-creating the old experiments with the aid of modern technology. She began by preparing garnet samples using an advanced thin-film technique called pulsed laser deposition, “a very fun way to make a material,” Kaczmarek said. It works by firing a laser at a target material in a vacuum chamber, vaporizing it. The material is then collected onto a substrate. The films Kaczmarek made were ultrathin, just a few nanometers thick, allowing her to easily “tune” the composition of the material and study the atomic arrangement in the crystals. She chose europium and thulium for the different sizes of their atoms, ensuring that they would separate into different sites in the crystal structure. Rosenberg’s initial work in growing these films laid a crucial foundation. Kaczmarek now had the tools and the setup to further investigate and solve the puzzle. “We grew the films, and we were immediately getting the same results as people 50 years ago”—the material had the same unusual magnetic properties, Kaczmarek said. “But we still didn’t have the visuals, the images of these atoms.” Unlike Callen in the early ’70s, Kaczmarek could visualize atoms doing what researchers long thought they were doing, with scanning transmission electron microscopy (STEM). This powerful technique focuses a beam of electrons through a material sample, collecting various signals to form an image. Using STEM in conjunction with atomic resolution spectroscopy, which can single out individual atoms, Kaczmarek could determine which elements were present in the garnet films she made and at what proportions. But it was no mean feat—since the theory she was trying to confirm predated the tools she was using, there were no protocols for indexing and interpreting data. “None of the characterization routes had been developed,” Kaczmarek said. “So I really had to inspect the crystal structure carefully and see where can I actually find information that’s useful to me, and where do I have to look somewhere else, or how do I know that what I’m looking at is showing me this or not?” After identifying specific patterns of atoms indicated in Callen’s theory, Kaczmarek quickly saw evidence that confirmed it. She described her joy when she first saw the rare-earth atoms occupying the sites Callan predicted, which was right around Thanksgiving in 2023. “We had just seen this image that clearly showed the europium are here, and the thulium are here. And of course, I immediately sent it to Geoff and Caroline, and I can’t forget, Geoff’s response was, ‘Happy Thanksgiving—I’m really gobbling this up!’” In the paper, Kaczmarek renames the automatic ordering of the elements that form the garnet crystals “magnetotaxial anisotropy”—combining the familiar term for magnetic materials with the Greek “taxis,” meaning arrangement or order. She found the old term, “growth-induced anisotropy,” strongly associated with bubble-memory garnets, outdated and limiting. “This is an important phenomenon, and I don’t think that it’s only a garnet thing. I think that it must exist elsewhere as well. It must exist in other materials,” Kaczmarek said. Bethanie Stadler, a professor of electrical and computer engineering at the University of Minnesota who was not involved in the study, underscored the significance of focusing on complex oxides, a broad category that includes perovskites, popular in materials research because of their applications in solar cells, lasers, and more. Garnets, complex oxides with a far more intricate crystal structure, offer a richer potential for discovery. “You could put half the periodic table into the garnet structure if you get the cation ratios right,” said Stadler, a DMSE alum, referring to the positively charged ions that occupy sites in the crystal. “If you can solve the mystery for garnets, you can apply these insights to other complex oxide structures.” Stadler also noted that the study’s findings on atomic ordering and anisotropy might lead to new ways to control how ions are arranged in crystals, which could affect their properties. “I find this study interesting because it is likely to get readers thinking of new ways to control anisotropy. For garnets, ‘growth-induced’ anisotropy is a term that has been used like a generic diagnosis, so clarifying its origins is important for engineering new technologically important properties.”

Crystallizing possibilities

Now that the theory that growing artificial garnets causes directionally dependent magnetism is confirmed, researchers have a new “knob” to fine-tune the material’s magnetic properties, Kaczmarek said. By adjusting growth conditions—such as deposition speed, temperature, and pressure—they can control the degree of anisotropy, tailoring materials for specific applications. “The processing will inherently change the end properties of our material,” Kaczmarek said. Such precision opens the door to advanced magnetic devices and technologies that could transform data storage, computing, and medical imaging. One key focus is spintronics, a new technology that leverages the “spin,” or momentum, of electrons for ultrafast data processing, lower power consumption, and greater data storage capacity. To support these applications, Ross is working on favoring out-of-plane magnetization in garnets and related complex oxide materials. This property, in which a material’s magnetism is aligned perpendicular to its surface, is crucial for spintronic and magneto-optical devices, which can be used for data storage, modulators, or sensors. “We’re hoping that by taking advantage of this phenomenon, this magnetotaxial anisotropy, we can make materials which will be ideal for high-speed magnetic memory and magneto-optical and photonic devices.” Ross wants to push the concept further still, creating garnet films with the desired properties, and transferring them to silicon substrates. “Everyone loves silicon. You can build your whole circuit on silicon, but you can’t grow garnet on silicon, at least not easily,” Ross said. “An argument is to grow the garnet separately, on a garnet substrate, and then peel it off and transfer it.” Affixing it to silicon, the foundation of most electronic devices, would incorporate the garnet film’s unique properties into mainstream electronics. Ross’s team is working with Professor Jeehwan Kim of MIT’s Department of Mechanical Engineering and DMSE on this thin-film transfer technology, “a hot topic right now,” she says. Stadler highlighted Kaczmarek’s rebranding of growth-induced anisotropy with the term “magnetotaxial,” suggesting it could inspire researchers to explore other ways of inducing this property. “I think the new term does start people thinking, ‘What, besides the growth surface, could introduce an anisotropy like that?’” Stadler said. “It’s going to be really fun to see what comes next for complex oxide anisotropy.” Beyond the applications, the “coolest thing” about the study is that it solved a 50-year-old mystery, said Ross. Her students dusted off an unconfirmed theory, applied modern experimental techniques, and closed this once-cold case. “You think of stuff that happened before your students were born and they’ve proven it,” Ross said. “That’s very cool.” This research was supported by the National Science Foundation’s Graduate Research Fellowship Program and the National Science Foundation Division of Materials Research.

Interactions between latent variables in count regression models

Special Issue/Methodological Challenges of Complex Latent Mediator and Moderator Models
Open access
Published: 26 August 2024

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Christoph Kiefer ORCID: orcid.org/0000-0002-9166-400X 1 ,
Sarah Wilker ORCID: orcid.org/0000-0002-0887-4672 2 &
Axel Mayer ORCID: orcid.org/0000-0001-9716-878X 1

In psychology and the social sciences, researchers often model count outcome variables accounting for latent predictors and their interactions. Even though neglecting measurement error in such count regression models (e.g., Poisson or negative binomial regression) can have unfavorable consequences like attenuation bias, such analyses are often carried out in the generalized linear model (GLM) framework using fallible covariates such as sum scores. An alternative is count regression models based on structural equation modeling, which allow to specify latent covariates and thereby account for measurement error. However, the issue of how and when to include interactions between latent covariates or between latent and manifest covariates is rarely discussed for count regression models. In this paper, we present a latent variable count regression model (LV-CRM) allowing for latent covariates as well as interactions among both latent and manifest covariates. We conducted three simulation studies, investigating the estimation accuracy of the LV-CRM and comparing it to GLM-based count regression models. Interestingly, we found that even in scenarios with high reliabilities, the regression coefficients from a GLM-based model can be severely biased. In contrast, even for moderate sample sizes, the LV-CRM provided virtually unbiased regression coefficients. Additionally, statistical inferences yielded mixed results for the GLM-based models (i.e., low coverage rates, but acceptable empirical detection rates), but were generally acceptable using the LV-CRM. We provide an applied example from clinical psychology illustrating how the LV-CRM framework can be used to model count regressions with latent interactions.

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Introduction

In psychology and the social sciences, researchers often model count outcomes accounting for latent predictors and their possible interactions. For example, Wilker et al. ( 2017 ) regressed symptom severity (i.e., how often did symptoms occur) of posttraumatic stress disorder on traumatic load, mental defeat, and their interaction. Both predictors were assessed using multiple items from psychometric questionnaires. Others studied the interactive effect of psychological distress and gender on problematic drinking behavior (i.e., number of alcoholic drinks; Rodriguez, Litt, & Stewart, 2020 ) or of callous traits and gender on antisocial outcomes (e.g., number of arrests; McMahon, Witkiewitz, Kotler, & The Conduct Problems Prevention Research Group, 2010 .

Such analyses often apply a generalized linear model (GLM; McCullagh & Nelder, 1998 ) using fallible predictors such as a sum score. Prominent options for count outcomes are Poisson or negative binomial regression (Hilbe, 2011 ). These are GLMs with a logarithmic link function and a Poisson or negative binomial distributed random component, which take the discrete and non-negative nature of count outcomes into account. The predictors are assumed to be observed without error and fixed by design, which is often not plausible for psychological measurements such as test scores.

While it seems to be a wide-spread approach to neglect measurement error in such analyses (Cheung, Cooper-Thomas, Lau, & Wang, 2021 ; Cortina, Markell-Goldstein, Green, & Chang, 2021 ), it is well known to have unfavorable consequences in GLMs: First, measurement error typically attenuates the regression coefficients towards zero, but in settings with multiple error-prone predictors both over- and underestimation can occur (Carroll, Ruppert, Stefanski, & Crainiceanu, 2006 ; Kiefer, & Mayer, 2021a ). Thus, attenuation bias complicates the identification of relevant product terms. Second, the reliability of the product term of two variables depends on their respective reliabilities and is typically lower than either of these (Bohrnstedt & Marwell, 1978 ; Busemeyer & Jones, 1983 ). Thus, products of fallible predictors strongly contribute to attenuation bias in parameter estimation. Consequently, there is a need for count regression models accounting for latent predictors and their (latent) interactions.

While latent interaction models with continuous outcomes (e.g., Kelava et al., 2011 ; Klein & Moosbrugger, 2000 ) with possible extensions for non-normally distributed latent variable indicators (e.g., Jin, Vegelius, & Yang-Wallentin, 2020 ) are well understood, latent interactions in count regression models are rarely discussed. A notable exception is the negative binomial multigroup structural equation model (NB-MG-SEM) by Kiefer and Mayer ( 2021a , 2021b ). The NB-MG-SEM allows interactions between latent continuous predictors and manifest categorical predictors by using a multigroup SEM approach. Recently, Rockwood ( 2021 ) proposed a generalized structural equation model (G-SEM) which can be used for the estimation of count regression models with latent predictors. While the G-SEM framework is very versatile, the formulation and implementation of Rockwood ( 2021 ) does not include product terms of the latent predictors.

In this paper, we contribute to the literature on latent interaction models for count outcomes in three ways: First, we present a general framework for a latent variable count regression model (LV-CRM) allowing for latent interactions. This framework is derived as extension of a GLM and also builds on the G-SEM framework by Rockwood ( 2021 ). Second, in three Monte Carlo simulation studies we compare the estimation accuracy of the proposed approach to GLM-based count regression models. Third, we provide an empirical example from clinical psychology to illustrate how the LV-CRM can be used to model count regressions with latent interactions in applied research.

Generalized linear models for count outcomes

In the following, we derive the LV-CRM as an extension of the GLM because many applied researchers are familiar with the GLM notation as well as GLM-based count regression models like Poisson or negative binomial regression. We start with describing the core elements of a GLM-based Poisson regression model, explain how interactions can be included within a GLM, and discuss the impact of measurement error in the predictors on the parameter estimation. In the next section, we introduce the LV-CRM as extension of the GLM allowing for latent covariates and latent interactions.

GLMs have been proposed by Nelder and Wedderburn ( 1972 ) and it can be shown that several well-known regression models, as for instance, the logistic regression model or the general linear model, are special cases of the GLM (McCullagh & Nelder, 1998 ). The key idea is that all these regression models can be decomposed into three main components: (a) a random component describing the conditional distribution of the outcome variable; (b) a weighted linear combination of the predictor variables (i.e., the linear predictor ), and; (c) a functional connection between the two, called the link function .

In principle, each component can be modified independently from the other two, which results in a very flexible way to model regressive dependencies among manifest variables.

Illustration of the interaction effects in linear and Poisson regression models without and with a dedicated interaction term. Red lines reflect linear approximations of the regression lines at \(\eta =1\) and \(\Delta \) indicates the slope of these approximations

Poisson regression model

GLMs for count outcomes are referred to as the family of Poisson regression models (e.g., Coxe, West, & Aiken, 2009 ). The standard Poisson regression model, while being parsimonious and comprehensible, is usually not suitable in applied scenarios. Thus, alternatives as the negative binomial regression are also part of the family of Poisson regressions. For a gentle introduction to Poisson regression models, see Coxe et al. ( 2009 ).

Consider a vector of N i.i.d. sampled outcome variables \(\varvec{y}= (Y_1,\dots ,Y_N)'\) , where the index \(i=1,\dots ,N\) indicates the individual observations, and for each individual the observation of m fixed predictor values \(\varvec{z}_i = (1, z_{1i}, \dots , z_{mi})\) with index \(j=1,\dots ,m\) . Then, according to McCullagh and Nelder ( 1998 ), the GLM-formulation of a standard Poisson reg-ression model involves the following three main components:

(a) the random component is Poisson distributed, \(Y_i \sim \mathcal {P}(\mu _{Y_i})\) , that is, we consider each observation \(Y_i\) of our count outcome to follow a Poisson distribution with expectation \(\text {E}(Y_i) = \mu _{Y_i}\) . The Poisson distribution comes with the property of equidispersion, meaning that the variance and the expectation of \(Y_i\) are identical. However, researchers often encounter overdispersed count outcomes, that is, the variance of \(Y_i\) exceeds its mean. In this case, an additional variance component can be introduced to the model, leading to Poisson-mixture distributions such as the negative binomial distribution (i.e., a Poisson-gamma mixture; Hilbe, 2011 ) or the Poisson-lognormal (PLN) distribution (Bulmer, 1974 ).

(b) The linear predictor \(\pi _i\) is defined as a weighted linear combination of the predictors \(\varvec{z}_i\) , where the weights \(\varvec{\beta } = (\beta _0,\beta _1,\dots ,\beta _m)'\) are called regression coefficients:

Below, we show how interactions between two or more predictors can be included in a GLM. It is important to note that the \(z_{ji}\) are treated as fixed constants. As a consequence, they are treated as perfectly reliable measures. However, this is not plausible if fallible scores of latent constructs (e.g., test scores from an intelligence test) are used as predictors, which can lead to attenuation bias in the estimated regression coefficients.

(c) For count outcomes, the expectation of the outcome variable \(\mu _{Y_i}\) and the linear predictor \(\pi _i\) are commonly connected via a logarithmic link function (short: log link), that is,

which naturally accounts for the lower bound of count outcomes at zero.

Left panel: Poisson regression ( black line ) of Y on the true scores of a predictor variable \(\eta \) ( black dots ). Right panel: Poisson regression ( black line ) of Y on fallible scores of the predictor variable (i.e., \(\eta \) plus a measurement error \(\epsilon \) ; black dots ). Dashed regression lines reflect deviations from the Poisson regression with true scores

Estimation of a Poisson regression model can be done via iteratively weighted least squares to find the maximum likelihood estimates and the corresponding standard errors. For more details, see Hilbe ( 2011 , Ch. 4)

Interactions in Poisson regression models

Whenever the effect of one predictor depends on the values of another, we can model this using product terms. In the GLM framework, product terms of the observed variables can be added as a new variable to the linear predictor, e.g., \(z_{3i} := z_{1i} \cdot z_{2i}\) . In a simple example with only two covariates and their interaction, the equation for the linear predictor is:

If, for instance, \(z_2\) is a binary trauma variable (e.g., \(z_2=1\) : trauma experienced vs. \(z_2=0\) not experienced) and \(z_1\) is age, we can compute the conditional regression of the count outcome on age given values of the trauma variable:

The first equation represents the relationship between the outcome Y and the predictor \(z_1\) (i.e., age) if a trauma was not experienced ( \(z_2=0\) ), and the second equation if a trauma was experienced, respectively.

It is important to note that Poisson regression models – just like other non-linear models – can contain interaction effects even if the coefficient of the product term is zero, that is, \(\beta _3=0\) . This phenomenon is called natural (or sometimes: model-inherent) interaction (Karaca-Mandic, Norton, & Dowd, 2012 ; McCabe, Halvorson, King, Cao, & Kim, 2022 ) and it is illustrated in Fig. 1 . In the upper right panel, the coefficient of \(z_1\) is identical for both \(z_2=0\) and \(z_2=1\) . However, for someone with a value of \(z_1=1\) the slope (as indicated by the red lines) varies considerably depending on the moderator. This is in contrast to the linear model (upper left panel) where the absence of a product term implies parallel lines. Thus, a product term can add to the complexity of the interaction pattern in a Poisson regression (lower right panel), but its absence is not equivalent to the absence of interaction.

The (linear) slopes of \(z_1\) , which are illustrated with the red lines in Fig. 1 , are called marginal effects and they are defined as the first derivative of the regression function. The effect of a third variable (i.e., \(z_2\) ) on the marginal effects of \(z_1\) , can then be defined as second-order mixed partial derivative (Kim & McCabe, 2022 ):

This definition of an interactive effect has two important properties: First, \(\zeta _{ijk}\) can be non-zero even if product terms are excluded (or \(\beta _3=0\) ). Second, \(\zeta _{jk}\) varies among individuals and is not a constant. That is, interaction effects vary as function of the predictors both involved and not involved in a product term.

Kim and McCabe ( 2022 ) propose three approaches to summarize and report the interaction effects \(\zeta _{jk}\) : First, it is possible to plug-in the observed predictor values and then compute summary statistics of the individual interaction effects, for example, an average interaction effect. Second, \(\zeta _{jk}\) is computed for representative points in the sample, for example, an “average” person (i.e., at the means of all covariates) and one standard deviation above and below this average. We will illustrate this approach in our empirical example below. Third, \(\zeta _{jk}\) is computed at substantively relevant points, for example, at a specific cutoff. It is possible to obtain standard errors for the interaction effects \(\zeta _{jk}\) by using the Delta method (Raykov & Marcoulides, 2004 ). Note, however, that if the interaction effects are computed at values estimated from the sample (e.g., sample mean), their sampling variance also has to be taken into account for reliable inferences (Liu, West, Levy, & Aiken, 2017 ).

Measurement error in the covariates

As stated before, a GLM assumes fixed predictors, which (a) are perfectly reliable and (b) do not vary from one sample to another. In psychological research, this is often not a realistic assumption, especially if predictors are randomly sampled, fallible measures of unobservable constructs (e.g., motivation, intelligence). If random measurement error in predictors is ignored, the regression coefficients get attenuated towards zero. This phenomenon known as attenuation bias (Carroll et al., 2006 ) is illustrated in Fig. 2 . While the left panel shows a Poisson regression based on the true values of a covariate \(\eta \) , the right panel shows the attenuation of the regression line as an effect of measurement error \(\epsilon \) added to the covariate. While attenuation bias is usually associated with attenuation towards zero, biases in all directions can be observed with multiple fallible covariates (Kiefer & Mayer, 2021a ; Carroll et al., 2006 ).

In the literature, several approaches have been proposed dealing with measurement error in covariates. Some approaches assume that the distribution of the latent variables is known (e.g., normally distributed with known mean and variance) and use this information to adjust the estimates of the regression coefficient (Guo & Li, 2002 ; Kukush, Schneeweis, & Wolf, 2004 ). Other approaches include a measurement model to estimate the distribution of the latent variables (Carroll et al., 2006 ; Skrondal & Kuha, 2012 ). These later approaches are called regression calibration . The measurement model is either included directly into a joint estimation with the regression coefficients or estimated first in a two-step procedure, where then distributional parameters or factor scores are used in the regression estimation (for more information on the two-step procedure, see Rosseel & Loh, 2022 ). However, these approaches are rarely extended to scenarios with product terms in non-linear models, with some exceptions treating this issue for logistic regression models (e.g., Carroll et al., 2006 , p.165). We are not aware of any contribution specifically addressing such adjustments focusing on both Poisson regressions and product terms.

Attenuation bias affects fallible predictors in general, but is likely to be exacerbated when product terms from fallible predictors are involved. This is because the reliability of the product term is usually lower than that of either of the interacting variables (Busemeyer & Jones, 1983 ). Bohrnstedt and Marwell ( 1978 ) show that for two predictors with reliability of .8, the reliability of the product term can drop below .6 (depending on their scaling and correlation). Nevertheless, it still seems to be a widespread approach to neglect measurement error in regression analyses containing interactions (Cheung et al., 2021 ; Cortina et al., 2021 ).

Latent variable count regression model

In this section, we introduce a latent variable count regression model (LV-CRM) framework for count regression models involving latent predictors, their interactions, manifest predictors, and possible latent-manifest interactions. For didactic reasons, we show how the LV-CRM can be derived as an extension of the GLM and therefore also stick to the common notation of the three main parts of the GLM.

We consider two steps to extend a GLM to a LV-CRM: (a) adding a measurement model as fourth model component and second, allowing latent variables, their interactions, and interactions between latent and manifest predictors as part of the linear predictor. Table 1 provides an overview and comparison of the GLM and the LV-CRM.

Note that the LV-CRM overlaps with the G-SEM framework (Rockwood, 2021 ), which, however, does not allow for interaction terms involving latent variables. The LV-CRM can be estimated with general purpose statistical software, for example, Mplus (Muthén & Muthén, 1998-2017 ), the GLLAMM approach in Stata (Skrondal & Rabe-Hesketh, 2004 ), or Stan (Stan Development Team, 2024 ). However, to our knowledge, the LV-CRM has not been previously described in the literature nor is there a technical documentation of how the LV-CRM is implemented, for example, in Mplus. Below, we will provide Mplus syntax for the empirical example as well as an open-source implementation in R.

Measurement model

In psychology and the social sciences, explicitly modeling measurement error and latent variables using a common factor technique (Bollen, 1989 ) is a popular approach. The key idea is that we have q measurements \(\varvec{w}_i=(W_{1i},\dots ,W_{q})'\) (e.g., items) intended to measure (multiple) latent variables (e.g., intelligence) and common factors \(\varvec{\eta }_i=(\eta _{1i},\ldots ,\eta _{pi})'\) with \(p \le q\) are introduced to model the correlations among the measurements:

where \(\varvec{\nu }\) is a \(q \times 1\) vector of intercepts; \(\varvec{\Lambda }\) is a \(q \times p\) matrix of factor loadings; and \(\varvec{\epsilon }_i\) is a \(q \times 1\) vector of measurement error variables. The observed indicators \(\varvec{w}_i\) are represented by a linear function of the latent variable plus measurement error. We assume that the measurement error variables \(\varvec{\epsilon }_i=(\epsilon _{1i},\ldots ,\epsilon _{qi})\) as well as the latent variables \(\varvec{\eta }_i\) are multivariate normally distributed with \(\varvec{\epsilon }_i \sim \mathcal {N}(\textbf{0}, \varvec{\Theta })\) and \(\varvec{\eta }_i \sim \mathcal {N}(\varvec{\mu }_{\eta }, \varvec{\Sigma }_{\eta })\) . Latent variables and measurement errors are independent from each other.

Identification of the model can be achieved through standard identification rules for structural equation models. That is, the scale of the latent variables has to be specified. Two popular methods for scaling the latent variables are (a) fixing one loading to one and one intercept to zero (typically for the first indicator) or (b) fixing the mean and variance of the latent variable to 0 and 1, respectively (cf. Kline & Little, 2023 ). Different scaling methods lead to equivalent models (i.e., point estimates are algebraic transformations of each other), but statistical inferences based on the Wald test can vary among scaling methods (Klopp, & Klößner, 2021 ).

Latent predictors and interactions

Now, we add the latent variables defined in the measurement model, and their possible interactions to the linear predictor component of the LV-CRM:

As denoted in the braces, the first part is equivalent to the linear predictor of the GLM. By adding the latent variables with regression coefficients \(\gamma _k\) to the predictor (i.e., the second part), we obtain a special case of the G-SEM (Rockwood, 2021 ). The third part adds latent interactions with regression coefficients \(\gamma _{kl}\) to the linear predictor Footnote 1 . The fourth part allows for interactions between latent and observed predictors with regression coefficients \(\omega _{jk}\) . Of course, some of the regression coefficients can be zero leading to more parsimonious models. The linear predictor in matrix notation is:

where \(\varvec{\gamma }\) is a \(p \times 1\) vector of regression coefficients; \(\varvec{\Gamma }\) is a \(p \times p\) upper triagonal matrix of regression coefficients; \(\varvec{\Omega }\) is a \(p \times m\) matrix of regression coefficients. Conceptually, the LV-CRM belongs to the regression calibration approaches mentioned earlier (Carroll et al., 2006 ; Skrondal & Kuha, 2012 ).

In the LV-CRM, the product terms are identified if the latent variables are identified. There are no additional assumptions or specifications necessary. This is achieved through the specification on an individual level: If the individual values of the latent variables are identified, then the individual product terms are also identified. Note that different estimation methods will approach this specification differently. For example, in a Bayesian framework, individual values of the latent variables will be sampled and directly plugged in the formulas. In contrast, the marginal maximum likelihood approach will integrate the latent variables out of the individual likelihood function. The next section provides an overview of the marginal maximum likelihood estimation.

Parameter estimation and standard errors

In this section, we provide a brief overview of maximum likelihood estimation of the proposed model. The overview is meant to give some intuition on the estimation technique and why no additional measurement models (e.g., as in a product-indicator approach; Kenny & Judd, 1984 ) or distributional assumptions (e.g., as in the LMS approach; Klein & Moosbrugger, 2000 ) are required for identification of the product terms. For a comprehensive illustration of the marginal likelihood technique and possible implementations, see Rockwood ( 2021 ) or Skrondal and Rabe-Hesketh ( 2004 , Ch. 6). Alternative estimation methods for the LV-CRM exist, for instance, using an EM algorithm as in Mplus (Muthén & Muthén, 1998-2017 ) or Bayesian methods (Asparouhov & Muthén, 2021 ; Stan Development Team, 2024 ).

Path model depicting the four components of a LV-CRM. Example shows the Poisson regression of outcome variable Y on the manifest predictors \(z_1\) to \(z_3\) , the latent predictors \(\eta _1\) and \(\eta _2\) , and their interaction term \(\eta _1 \cdot \eta _2\)

For didactic reasons, we will restrict ourselves to describing the maximum likelihood estimation for the model depicted in Fig. 3 . This is actually the model used for simulation study 2 and very similar to the empirical example. The case-wise log-likelihood function for this model can be written as:

where \(y_i\) , \(\varvec{z}_i = (z_{1i}, z_{2i}, z_{3i})\) , and \(\varvec{w}_i = (W_{1i}, W_{2i}, W_{3i},\) \( W_{4i}, W_{5i}, W_{6i},)\) are the individual values of the observed variables. Since the values of the latent variables \(\eta _1\) and \(\eta _2\) are not observed, they are integrated out.

There is no closed-form solution for the log-likelihood function and hence it has to be approximated through numerical techniques:

where M is the number of integration points, \(\omega _j\) is an integ-ration weight, and \(\eta _{1j}^*\) and \(\eta _{2j}^*\) are the integration points, respectively. An advantage of this procedure is that it provides a fixed set of values for the latent variables for each person in each iteration. Similar to the procedure in a GLM, we can use these latent variable values to compute product terms within the linear predictor. This is why the latent interaction term is presented as part of the linear predictor in Fig. 3 , but not as part of the measurement model. The product term is only part of the linear predictor and the linear predictor is only part of the density function of the outcome variable \(f(y_i|\varvec{z}_i, \eta _{1j}, \eta _{2j})\) . Now, for each part sum of the approximated case-wise likelihood, we can simply compute the linear predictor \(\pi _i(j)\) as a function of the integration points:

These models can become computationally demanding if the number of latent variables and thus the integration points rises, and we will discuss some approaches to reduce the computational burden. Standard errors can be derived using standard maximum likelihood theory, but this step is also computationally demanding as the second order derivatives of the log-likelihood have to be numerically approximated, too.

Simulation studies

We conducted three Monte Carlo simulation studies to examine the performance of the LV-CRM framework under various empirical conditions and compared it to GLM-based Poisson or negative binomial regressions. From a substantive point of view, it is most interesting under which conditions the potential gains from the LV-CRM (e.g., reducing attenuation bias, increasing power) outweigh the costs (e.g., additional distributional assumptions, potential bias and numerical instability for insufficient sample sizes). Thus, we aligned our simulation studies with the aim to provide guidelines for substantive researchers which model to prefer under which conditions. The first simulation study focused on the extent of attenuation bias in a Poisson regression model with two latent variables and their interaction. It examines the question of how much bias one can expect given certain reliabilities of the sum scores, while still being computationally feasible to replicate by the interested reader. The second simulation study focused on two questions, namely, (a) if and how much attenuation bias can spill over to regression coefficients of perfectly reliable measures, and (b) how the statistical inferences from the LV-CRM perform and how they compare to GLM-based inferences. As this simulation study includes standard error estimation for the LV-CRM, it is computationally more burdensome. The third simulation study focused on attenuation bias in more complex scenarios, where we considered three latent variables and their two-fold interactions. We examined attenuation bias for different combinations of product term coefficients as well as correlational patterns among the latent variables. Due to the required three-dimensional numerical integration, this simulation was computationally demanding, too. The corresponding R code as well as the final results for all three simulation studies are available from OSF via https://osf.io/q7knc .

Simulation study 1

The main focus of our first simulation study is to investigate the effect of different magnitudes of reliability of the fallible predictors on attenuation bias and how well the LV-CRM can de-attenuate the estimated regression coefficients. We pursue this focus with a small-scale simulation that can be reproduced by the interested reader within reasonable time. In the simulation studies 2 and 3, we will investigate additional aspects of statistical inferences and higher-dimensional numerical integration which are computationally more demanding. Final results for all three simulation studies are included in the OSF repository.

In this simulation study, we used a model with two latent variables, \(\eta _1\) and \(\eta _2\) , and their interaction as predictors of the outcome variable Y in a Poisson regression model. The linear predictor was:

where we are particularly interested in the estimation of the product term coefficient \(\gamma _{12}\) .

The latent variables were simulated as standard bivariate normally distributed with a correlation of \(r=.3\) and measured with three indicators each. We also computed sum scores as fallible substitutes of the latent variables over the three indicators, respectively. The sum scores were z -standardized for comparability with the latent variables. The reliabilities of the sum scores were manipulated independently by altering the measurement error variances of the indicators. We investigated the six reliability combinations for the sum scores of both latent variables, considering the reliabilities of 0.7, 0.8, and 0.9 respectively. Additional design factors where the sample size ( \(N=100,~200,~500,~1000\) ) and the size and direction of the interaction parameter ( \(\gamma _{12}=-0.3,~0,~0.3\) )

We estimated the model with both a LV-CRM (where the means of the latent variables were fixed to 0 and the variances to 1) and a GLM (with z -standardized sum scores) and investigated and compared the bias and efficiency of the estimated product term parameter \(\hat{\gamma }_{12}\) (or \(\hat{\beta }_4\) in the GLM, respectively).

Simulation study 1: (Relative) bias of estimated product term coefficients in the LV-CRM ( purple ) and the GLM ( green ). The upper panel shows bias for conditions with \(\gamma _{12}=0\) , the lower panel shows relative bias for conditions with \(\gamma _{12}\ne 0\) . Columns reflect the six combinations of reliabilities of the sum scores, rows reflect the size and direction of the product term coefficient, x -axis reflect sample size N

Convergence

We ran the simulation with \(R=1000\) replications including non-converged solutions first in order to examine the convergence behavior of both approaches. Both approaches yielded convergence rates of virtually 100% in this simulation. Only in six out of 72 conditions was there one out of 1000 replications where the LV-CRM did not converge. These six conditions had a positive product term coefficient in common, but were unsystematic regarding the other design conditions (i.e., large and small sample sizes, high and low reliabilities). Thus, convergence seemed to be no issue for the specified model in sample sizes from \(N=100\) upwards.

The following analyses are based on a second run of the simulation with \(R=1000\) replications excluding non-converged solutions, that is, if one of both models did not converge the replication was repeated until both models converged.

We investigated the bias of the estimated product term coefficient \(\hat{\gamma }_{12}\) in the LV-CRM and \(\hat{\beta }_4\) the GLM, respectively. The results are presented (a) as bias for cases where the true parameter \(\gamma _{12}=0\) and (b) as relative bias for cases where the true parameter was non-zero, i.e., \(\gamma _{12}\ne 0\) . The results are illustrated in Fig. 4 Both models yielded very small biases in conditions where the true parameter \(\gamma _{12}\) was zero. For the GLM, the bias ranged between \(-0.006\) and 0.002. The largest negative bias of about \(-0.015\) was found for the LV-CRM in a condition with low reliabilities (both 0.7) and a small sample size of \(N=100\) . The upper bound of the bias for the LV-CRM was 0.000. Overall, the LV-CRM tended to underestimate the true parameter more than the GLM approach. This is not surprising, as measurement error is expected to attenuate the estimated regression coefficients of a GLM towards zero and in conditions with a true coefficient of zero, the attenuation is ’favorable’ for the estimation of this zero.

The unfavorable effects of attenuation become clear, when looking at the relative bias of the estimated product term coefficient in scenarios where the true parameter is not zero. The relative bias of the estimated product term parameter in the GLM approach ranged between \(-5.6 \%\) and \(-33.2 \%\) . That is, even in scenarios with highly reliable score variables (both .9), we found at least 5% underestimation. If at least one predictor had a reliability of .8 or lower, the underestimation was about 10% or more. On the other hand, the LV-CRM performed better and relative bias ranged between \(-4.9\%\) and \(8.2\%\) . Interestingly, overestimation of the product term coefficient occurred in scenarios with positive product term coefficient, highly reliable score variables, and small sample sizes. Overall, the LV-CRM provided more accurate estimates (i.e., less bias) for the product term coefficient than the GLM. For sample sizes of \(N=200\) or larger, the LV-CRM yielded relative bias below \(\pm 5 \%\) under all conditions.

Relative efficiency

The results for the relative efficiency of the LV-CRM compared to the GLM (i.e., ratio of the respective RMSE) are presented in Fig. 5 . In conditions with a true product term coefficient of \(\gamma _{12} = 0\) , the relative efficiency of the LV-CRM approach compared to the GLM approach ranged between 1.048 and 1.270. That is, the RMSE of the LV-CRM approach is about 4.8–27% higher than that of the GLM. Again, that is not surprising given the ’favorable’ effect of the attenuation bias in these conditions.

Simulation study 1: Relative efficiency of estimated product term coefficient in the LV-CRM compared to GLM (i.e., RMSE of LV-CRM divided by the RMSE of the GLM). Columns reflect the six combinations of reliabilities of the sum scores, rows reflect the size and direction of the product term coefficient, x -axis reflect sample size N

Simulation study 2: Spill-over effect \(S(\hat{\beta }_1, \hat{\beta }_2, \hat{\beta }_3)\) of estimated regression coefficients in the LV-CRM and in the GLM. Columns reflect the six combinations of reliabilities of the sum scores, rows reflect the size and direction of the product term coefficient, x -axis reflect sample size N

In conditions with a true interaction parameter of \(\gamma _{12} \ne 0\) , the relative efficiency ranges from 0.304 to 1.622. As can be seen in Fig. 5 , the LV-CRM is typically more efficient in scenarios with a negative interaction parameter, especially with larger sample sizes. With a positive product term coefficient, the LV-CRM is typically more efficient if at least one predictor has a reliability of 0.7 or both reliabilities were .8 (with few exceptions in sample sizes of \(N=100\) ). However, with increasing reliability of the fallible score variable the LV-CRM was less efficient than the GLM.

Simulation study 2

In the second simulation study, we extended the design of our first study in two regards: First, we investigated whether attenuation bias can have a spill-over effect on other regression coefficients, for instance, those of perfectly reliable predictors. Second, we examined whether the bias reduction in the LV-CRM approach also comes with improved statistical inferences, for example, an increase of power to detect non-zero product term coefficients. Thus, we computed 95% confidence intervals (CIs) and the empirical detection rate for each condition.

The simulation design followed our first simulation study with a few additions: First, three additional manifest and perfectly reliable predictors were added to the regression model. These predictors were generated as independent from each other and from the latent variables. This was done to investigate potential spill-over effects of the fallible score variables.

The linear predictor was

Second, standard errors, confidence intervals, and the empirical detection rate for the interaction parameter \(\gamma _{12}\) were computed. Third, the random component was chosen as negative binomial instead of a Poisson distribution. This is a more realistic scenario, as it incorporates additional variance in the outcome not explained for by the predictors (which is usually the case in applied settings), but the estimation is slightly more demanding. It is also closely related to our empirical example below, where we use a negative binomial regression model.

Spill-over effect

We used the Euclidean norm of the biases of the three regression coefficients of the observed covariates (i.e., \(B(\hat{\beta }_1)\) , \(B(\hat{\beta }_2)\) , \(B(\hat{\beta }_3)\) ) to get an overall evaluation of possible spill-over effects, that is,

The results are summarized in Fig. 6 . We also computed bias and relative efficiency of the remaining coefficients (i.e., of the latent variables and the interaction term), but do not illustrate the results here as they closely resemble our findings from the first simulation study. The complete results can be found in the OSF repository.

Overall, the results indicate no spill-over effect of the fallible score variables on the estimated regression coefficients of the perfectly reliable covariates. The spill-over effect \(S(\hat{\beta }_1, \hat{\beta }_2, \hat{\beta }_3)\) ranged from 0.0004 to 0.0149 for the GLM and from 0.0007 to 0.0149 for the LV-CRM. The highest values were obtained in scenarios with mixed reliabilities (i.e., one high, one low) on both fallible scores and rather low sample sizes. However, the corresponding regression coefficients appeared virtually unbiased under all conditions.

Coverage and empirical detection rate

We examined the coverage rate (i.e., the proportion of CIs including the true parameter value), and the empirical detection rate (i.e., the proportion of CIs not including zero) for the 95 % confidence intervals (CI) of the interaction parameter \(\gamma _{12}\) estimated with both approaches (i.e., \(\hat{\gamma }_{12}\) in the LV-CRM and \(\hat{\beta }_6\) in the GLM). The results are summarized in Fig. 7 .

In scenarios where the true interaction parameter was \(\gamma _{12}=0\) , both the coverage rate and the empirical detection rate (i.e., the type I error rate in these scenarios) were acceptable for both approaches. For the GLM, the actual coverage rate of the CIs ranged between 0.918 and 0.948 and the empirical detection rate between 0.052 and 0.085, respectively. For the LV-CRM, the actual coverage rate of the CIs ranged between 0.923 and 0.957 and the empirical detection rate between 0.043 and 0.077, respectively.

Simulation study 2: Coverage rates ( upper panel ) and empirical detection rates ( lower panel ) of estimated interaction coefficient \(\hat{\gamma }_{12}\) in the LV-CRM and in the GLM. Columns reflect the six combinations of reliabilities of the sum scores, rows reflect the size and direction of the product term coefficient, x -axis reflect sample size N

In contrast, in scenarios where the true product term coefficient was not zero (i.e., \(\gamma _{12}\ne 0\) ), coverage and empirical detection rate yielded diverging results. For the GLM, the actual coverage rate of the CIs ranged between 0.199 and 0.951 and the empirical detection rate (i.e., the power in these scenarios) between 0.127 and 1.000, respectively. Notably, the coverage rate was more accurate for small interaction sizes (i.e., \(\gamma _{12} = |0.1|\) ), but barely acceptable for larger interaction sizes (i.e., \(\gamma _{12} = |0.3|\) ). For the LV-CRM, the actual coverage rate of the CIs ranged between 0.904 and 0.966 and the empirical detection rate between 0.121 and 1.000, respectively. Overall, the power was similar for both approaches. On average, the power was 0.7% higher for the LV-CRM, where the differences in power between the two approaches ranged from -3.9% (i.e., higher power in the GLM) to 8.2% (i.e., higher power in the LV-CRM).

Simulation study 3: (Relative) bias of estimated interaction coefficients in the LV-CRM ( yellow , blue , magenta ) and the GLM ( green , purple , orange ). Numbers in the legend refer to the index of the estimated regression coefficient, for example, GLM_12 reflects the estimate of \(\gamma _{12}\) in a GLM. The upper panel shows bias for conditions where all interaction coefficients were zero, the lower panel shows relative bias for the remaining conditions. In conditions with mixed coefficients, (relative) bias for \(\gamma _{12}=0\) is not shown. Columns reflect the correlational patterns among the LVs, rows reflect the size and direction of the product term coefficients, x -axis reflects different combinations of reliabilities of the sum scores

When it comes to statistical inferences, these findings indicate that attenuation bias in the GLM is somewhat compensated for by an overconfident (i.e., too small) standard error. As a result, hypothesis testing seemed to work reasonably well, but the confidence intervals were too narrow and did often (i.e., up to 80.1%) not include the true parameter value. In contrast, the LV-CRM yielded unbiased point estimates and accurately accounted for multiple sources of uncertainty (e.g., measurement error, regression residual) resulting in wider CIs (compared to the GLM). Thus, null hypothesis testing would be expected to work with both approaches, but substantive interpretation of the CI is more reliable with the LV-CRM.

Simulation study 3

In the third simulation study, we focused on scenarios with three latent variables and their two-fold interactions. Our goal was to investigate the extent of attenuation bias in this complex settings given different combinations of reliability, correlations, and interactional patterns among the latent variables. Similar to the first simulation study, we restricted ourselves to consider bias and relative efficiency of the estimated interaction parameters alone and did not investigate statistical inferences in order to keep the simulation computationally feasible.

The design of the simulation study is similar to the first simulation study, but with three latent variables and their three two-fold interactions. The linear predictor was:

We manipulated the following three factors: First, reliability of the sum scores of each latent variable could take the values 0.7 or 0.9, resulting in eight reliability combinations. Second, we investigated four different correlational patterns among the latent variables. These four patterns where (a) small negative correlations ( \(r=-.3\) ) among all LVs, (b) small positive correlations ( \(r=.3\) ) among all LVs, (c) no correlations ( \(r=0\) ) among all LVs, and (d) mixed correlations (negative, positive, null) among the LVs. Third, we investigated four different interactional patterns. These were (similar to the correlations) (a) small negative interaction coefficients ( \(\gamma =-.3\) ) for all LVs, (b) small positive interaction coefficients ( \(\gamma =.3\) ) for all LVs, (c) no product-term induced interaction ( \(\gamma =0\) ) for all LVs, and (d) mixed interaction coefficients (negative, positive, null) for the LVs. We did not investigate different sample sizes in this simulation, but choose a single sample size of \(N=500\) throughout all conditions.

Simulation study 3: Relative efficiency of estimated interaction coefficients in the LV-CRM compared to GLM (i.e., RMSE of LV-CRM divided by the RMSE of the GLM). Numbers in the legend refer to the index of the estimated regression coefficient, for example, GLM_12 reflects the estimate of \(\gamma _{12}\) in a GLM. Columns reflect the correlational patterns among the LVs, rows reflect the size and direction of the product term coefficients, x -axis reflects different combinations of reliabilities of the sum scores

The results on attenuation bias are presented (a) as bias for scenarios where all interaction coefficients were zero and (b) as relative bias for non-zero interaction coefficients. An overview of the results is given in Fig. 8 .

In conditions where all three interaction coefficients were zero, the bias ranged between -0.002 and 0.002 for the GLM and between -0.004 and 0.003 for the LV-CRM. As in the first simulation study, this result is not surprising given the ’favorable’ effect of attenuation bias in these conditions.

In conditions where interaction coefficients could differ from zero, we found a common pattern of relative bias in most conditions, but with a notable exception (i.e., all correlations and interactions positive), which we will discuss separately. The common pattern shows a substantial relative bias for all three estimated interaction coefficients in the GLM (between -0.451 and 0.016), but comparably low relative bias for the estimated interaction coefficients for the LV-CRM (between -0.112 and 0.020).

However, in conditions with positive correlations among the LVs and three positive product term coefficients, a rather unsystematic pattern of relative bias occurred – as displayed in Fig. 8 . Here, relative biases in both directions were observed, that is, between -0.225 and 0.261 for the GLM and between -0.169 and 0.190 for the LV-CRM. In order to examine if this pattern was caused by the medium sample size and possible estimation issues, we re-run these conditions with a larger sample size of \(N=2000\) . However, we found the same pattern again. We are not sure why the relative bias behaves so differently under these conditions, but suspect an unfavorable combination of multicollinearity of the latent variables and their interaction terms, measurement error, and rather steep conditional effects (i.e., simple slopes) leading to highly dispersed outcome values. However, it shows that attenuation bias can have rather unexpected effects in complex scenarios involving multiple latent variables.

The results for the relative efficiency of the estimated interaction coefficients in the LV-CRM compared to the GLM were similar to our findings from simulation studies 1 and 2 and are therefore not discussed in detail again. An overview is given in Fig. 9 and the complete results are available from the OSF repository.

Empirical example

We provide an empirical example from clinical psychology to illustrate how the LV-CRM framework can be applied to model count regressions with latent interactions in applied settings. Wilker et al. ( 2017 ) examined the effects of trauma load, mental defeat, and their interaction on symptom severity (i.e., incidence of symptoms) of posttraumatic stress disorder (PTSD) and dissociation in Ugandan war survivors.

Theoretical background

The experience of traumatic events such as war, torture, sexual violence, accidents, or natural disasters can lead to the development of PTSD. The disorder is characterized by intrusive re-experiencing of the traumatic events, avoidance of trauma reminders, persistent alterations of mood and cognition, and a state of elevated arousal (American Psychiatric Association, 2022 ). In addition to these symptoms, survivors of multiple and interpersonal trauma are at elevated risk to show dissociative symptoms, which include feelings of derealization (e.g., feeling as if the own experience is not real), depersonalization (e.g., feeling as if being outside the own body), dizziness, and an incapability to move (Schauer & Elbert, 2010 ; Vermetten & Spiegel, 2014 ).

Importantly, after a single or few traumatic events, the majority of individuals do not develop trauma-associated psychopathology (Kessler et al., 2005 ). Whether an individual will develop mental health symptoms after a traumatic event largely depends on individual risk and resilience factors as well as on trauma-related predictors (Kessler et al., 2021 ). However, research from post-conflict settings indicates that with an increasing number of different types of traumatic events (termed traumatic load) almost every individual will develop mental health symptoms, and individual risk and resilience factors only play a subordinate role (Neuner et al., 2004 ; Wilker et al., 2015 ).

Peritraumatic cognitive processes, referring to thoughts which occur at the time of the trauma, have been identified to influence both the memory and the appraisal of the traumatic event. Therefore, they represent risk factors for trauma-associated psychopathology and important targets for trauma-focused interventions which aim at the modification of trauma memories and associated negative cognitions. One important peritraumatic cognitive process is termed mental defeat (Kleim, Ehlers, & Glucksman, 2012 ) and refers to a loss of mental resistance and human dignity during the trauma (Dunmore, Clark, & Ehlers, 1999 , 2001 ). The experience of mental defeat during a trauma is associated with the development of permanent negative cognitions about the self (e.g. “I am weak” or “I am destroyed”) and the world (e.g., “I can trust nobody”), which are known to be important symptoms of PTSD. At the same time, they lead to increased avoidance of trauma-associated memories and thereby lead to the maintenance and chronification of psychopathology (Dunmore et al., 2001 ; Ehlers et al., 1998 ).

While there is a lot of evidence indicating that the peritraumatic cognitive process of mental defeat is a central risk factor for PTSD in individuals from relatively peaceful, industrialized countries, research from post-conflict settings on mental defeat was completely lacking. Since the burden of PTSD is much higher in post-conflict settings compared to industrialized countries (Charlson et al., 2019 ), research from this context is urgently needed to better understand factors central to trauma-associated psychopathology and its treatment in this context. Therefore, Wilker et al. ( 2017 ) conducted a study to investigate whether mental defeat would be an important predictor of PTSD and dissociative symptoms in a post-conflict population from northern Uganda. In more detail, they investigated the interplay of trauma load and mental defeat on PTSD risk, PTSD symptoms, and dissociative symptoms. Because previous research showed that individual predictors become less important at higher levels of trauma load, potential trauma load \(\times \) mental defeat interaction effects were of particular interest to the study.

The description of the methods is taken from Wilker et al. ( 2017 , pp. 3–5). For the complete methods, the reader is referred to the original article.

Data collection took place in villages of Nwoya district in northern Uganda. This area was severely affected by the war between the Lord’s Resistance Army (LRA) rebel group and the Ugandan governmental forces, which lasted almost two decades. The atrocities committed during this war included forced recruitment and abductions of children and young adults, killings, mutilations, and sexual offenses. Data collection took place in 2013, 8 years after the cease-fire agreement between the LRA and the governmental troops in 2005. The final sample of \(N=227\) was 54% female, with a mean age of 33.29 (SD = 10.56, range = 18–62).

Trauma exposure was assessed by means of a 62-item event list. This event list comprised general traumatic experiences (e.g., natural disasters, accidents), war-related traumatic events (e.g., being close to combat), as well as events specific for the LRA conflict (e.g., being forced to kill somebody by the LRA). We calculated the number of different traumatic event types experienced to assess the amount of trauma exposure (traumatic load). As previously shown in the same sample, the retest reliability of this variable was \(r = .82\) (Wilker et al., 2015 ) and, thus, was treated as a latent predictor using a single indicator approach in our analysis.

The extent of mental defeat was assessed for the worst traumatic event using the Mental Defeat Questionnaire (MDQ) in the form of an interview (Dunmore et al., 1999 , 2001 ). The MDQ comprises 11 unipolar items (e.g., “I lost any will-power”, “I felt destroyed as a person”) and requires responses on a five-point Likert-type scale ranging from not at all to very strong. The MDQ showed a good internal consistency in the present sample (Cronbach’s \(\alpha = .89\) ) and was modeled as a latent predictor using a multiple indicator approach in our analysis.

The main outcome of our analysis were dissociative symptoms assessed by means of the Shutdown Dissociation Scale (Shut-D; Schalinski, Schauer, & Elbert, 2015 ). The Shut-D includes 13 unipolar items (e.g., “Have you fainted?” “Have you felt like you were outside of your body?” “Have you felt suddenly weak and warm?” “Have you felt nauseous? Have you felt as though you were about to throw up? Have you felt yourself break out in a cold sweat?”) investigating current bodily dissociative symptoms for the past 6 months. Participants were requested to answer on a four-point scale ranging from 0 ( never ) to 3 ( several times a week ). Thus, the scale score acts as a proxy of the incidence of dissociative symptoms and behaves similarly as a count variable, that is, the lower bound represents zero symptom occurrences, the variable only takes non-negative integer values, and a certain amount of heteroscedasticity is present. Thus, Wilker et al. ( 2017 ) handled the outcome as a count variable. The Shut-D showed a high internal reliability in the present study (Cronbach’s \(\alpha = .91\) ).

Statistical analysis

Wilker et al. ( 2017 ) compared models of varying complexity (i.e., with and without including the covariates age, sex, and age at worst event and with and without considering potential trauma load - mental defeat product terms). In this study, in order to investigate the differences between the GLM and the LV-CRM, we calculated the full model. Accordingly, our model included the main effects of sex, age, and age at worst event. Further, trauma load, mental defeat as well their interaction were included as predictors in the regression models.

Negative binomial regression

As in the original study by Wilker et al. ( 2017 ), we estimated a negative binomial regression. That is, the outcome variable \(Y_i\) (i.e., the Shut-D score) is linked to the linear predictor with a log link and is assumed to follow a negative binomial distribution.

The linear predictor in this model was

In addition, we used the LV-CRM framework to carry out the same analysis, but including a measurement model for the latent trauma load ( \(\eta _{\text {TL};i}\) ) and mental defeat ( \(\eta _{\text {MD};i}\) ) variables in order to account for measurement error:

Both scales were fixed to a mean of zero and a variance of one and, consequently, all intercepts and loadings as well as the latent covariance were estimated freely.

Note that we modeled trauma load \(\eta _{\text {TL};i}\) using a fixed-reliability single indicator approach as proposed by Savalei ( 2019 ) for two reasons: First, trauma load is not a traditional psychometric variable, but the items reflect different traumatic event types. The items are not meant to measure a common factor and are likely to be uncorrelated to some extent (i.e., experiencing a natural disaster is not necessarily correlated to having an accident). Thus, a multiple indicator approach would not have been appropriate. Second, the retest reliability of 0.82 (Wilker et al., 2015 ) indicates that trauma load cannot be assessed exactly, meaning there is some kind of measurement error involved. According to our simulation studies, we would expect a substantial attenuation bias on the product term coefficient given this level of reliability and, thus, explicitly controlling for this measurement error seems warranted. In order to fix the reliability of trauma load to 0.82, we constrained its measurement error variance to:

where \(\text {Rel}_{\text {TLS}} = 0.82\) . Then, the linear predictor for the LV-CRM was

where the standardized test scores for mental defeat and trauma load are replaced with the corresponding latent variables.

Conditional regressions for the relation between latent mental defeat (MDQ) and dissociative symptoms (SDQ) given several values of trauma load (at 2 SD below mean in dark green ; at 1 SD below mean in light green ; at mean in yellow ; at 1 SD above mean in orange ; at 2 SD above mean in red ). Black dots indicate value combinations where the interaction effect \(\zeta \) is significant

In a first step, we inspected and compared the estimated regression coefficients from both models. Table 2 shows the regression coefficients, their standard errors, and p values of the estimated GLM and the LV-CRM, respectively. As can be seen, the estimated coefficients for trauma load, mental defeat, and their interaction are larger if measurement errors are considered, as in the LV-CRM. These results are in line with our simulation results on attenuation bias and illustrate that the GLM is likely to underestimate the true parameter effects even if the reliability of the latent variables is relatively high.

Notably, the coefficient of the product term of trauma load and mental defeat was not significant in the GLM. Therefore, Wilker et al. ( 2017 ) identified a main effect model as the most parsimonious model with the best data fit and reported their results from this model. By contrast, the LV-CRM was able to identify a significant coefficient for the product of trauma load \(\times \) mental defeat. Note that interaction effects are likely to be present in both models because of the phenomenon of natural interaction, that we discussed above. However, the significance of \(\hat{\gamma }_{12}\) in the LV-CRM indicates that the product term of the latent variables can help explain additional complexity of the interaction pattern.

In a second step, we looked at the specific interaction effects, that is, we computed \(\zeta \) from Eq. 1 with respect to \(\eta _{\text {MD}}\) and \(\eta _{\text {TL}}\) . Figure 10 shows conditional regression plots, that is, the regression of the dissociative symptoms Y on mental defeat \(\eta _{\text {MD}}\) conditional on values of trauma load \(\eta _{\text {TL}}\) . The illustration is similar to simple slopes in OLS regression, but shows non-linear relationships. The color indicates the extend of trauma load (green to red); the latent variable \(\eta \) (MDQ) has a standardized scale, that is, mean of 0 and standard deviation of 1. The covariates age, sex, and age at worst event were fixed to their means. If trauma load was below the average or at the average (green and yellow lines; 1 and 2 SD below average and average, respectively), there was a (strong) positive association between mental defeat and dissociative symptoms. However, the association declines at an average trauma load and above average (yellow and orange line), and changes sign at 2 SD above average.

Slope of \(\eta _{\text {MD}}\) for the relation between latent mental defeat (MDQ) and dissociative symptoms (SDQ) depending on \(\eta _{\text {TL}}\) at \(\eta _{\text {MD}}=0\) and remaining covariates at their means. Vertical lines indicate where the interaction effects (i.e., slopes of the slopes curve) were computed

We computed the interaction effect \(\zeta \) , to gain more information if and how much the slopes of the regression of dissociative symptoms on mental defeat change due to the trauma load. The results are presented in Table 3 . Again, we fixed the covariates age, sex, and age at worst event to their means. We can see, that the (instantaneous) positive change in slopes due to trauma load is especially significant for values below and at average on both latent variables. This means that for persons with below or at average values of mental defeat the relationship to dissociative symptoms depends on the amount of trauma load, at least if trauma load is also below or at average. Vice versa, the relationship between mental defeat and dissociative symptoms does not (significantly) depend on trauma load, if trauma load is above average.

These results provide a first glimpse into the highly complex interactional patterns. For example, the slope of mental defeat depending on trauma load for a person with an average value of mental defeat (i.e., \(\eta _{\text {MD}}=0\) ) is illustrated in Fig. 11 . We reported the interaction effect (i.e., slope of the slope curve) from representative values of the trauma load variables (i.e., slopes where vertical lines intersect with the slope curve), but there are alternative techniques to summarize the interactional pattern. However, such techniques (e.g., average marginal/interaction effect) have to be adapted for models with latent variables.

Discussion of empirical example

Previous research showed that at higher levels of trauma load, the interindividual variability in trauma-associated symptoms decreases and individual risk factors may only play a subordinate role (Kolassa et al., 2010 ; Mollica, McInnes, Pool, & Tor, 1998 ; Neuner et al., 2004 ; Wilker et al., 2015 ). This should be reflected by significant interactions between risk factors, such as peritraumatic mental defeat, and trauma load on the outcome variable. While this effect was only present at a trend level when employing classical negative binomial regression models, the novel method introduced in this paper allowed us to discover such interaction effects.

At the same time, the strong importance of both trauma load and mental defeat as predictors of Shutdown dissociation were replicated by the novel analyses. Due to the de-attenuation, the effects were even stronger than reported in the original analyses.

In psychology and the social sciences, interactions between latent predictors in count regressions are often of interest. While it is well known that using fallible scores and not accounting for measurement error generally leads to attenuation bias in the estimated regression coefficients (Carroll et al., 2006 ), the extent of these effects has not been previously studied for count regression models. In this paper, we introduced the latent variable count regression model (LV-CRM) framework. We examined its performance regarding point estimation as well as statistical inferences using simulation studies and illustrated its use in an empirical example from clinical psychology.

In our simulation studies, we found that severe attenuation bias (i.e., relative bias below \(-10 \%\) ) can occur for the product terms even if the fallible scores had considerably high reliabilities (i.e., both 0.9). For non-zero product term coefficients, the estimated parameters from the GLM were attenuated up to \(-33 \%\) . Attenuation bias this high was observed both in scenarios with two and three latent variables and their respective interactions. In contrast, the LV-CRM yielded virtually unbiased estimates under most conditions and provided de-attenuated point estimates. In our third simulation study, we found a notable exception to this rule: In scenarios with three positively correlated latent variables and three positive product term coefficients, the relative bias fluctuated rather unsystematically for both the GLM and the LV-CRM with larger biases for the newly proposed approach. For product term coefficients of zero, however, both approaches worked equally well, with a slight advantage for the GLM due to the attenuation bias.

With regard to the relative efficiency of the point estimates, we found similar results. That is, for scenarios with a product term coefficient of zero the GLM was slightly more efficient, as the attenuation bias has a ’favorable’ effect in this case. In scenarios with non-zero product term coefficients, however, the LV-CRM was often considerably more efficient than the GLM, especially if reliabilities of 0.8 or below were involved.

Our second simulation study also investigated statistical inferences for the GLM and the LV-CRM. Interestingly, we found that the empirical detection rates (i.e., type I error rates and power) were acceptable and on the same level for both approaches. In the GLM, the biased point estimates are compensated by overconfident standard error estimates, that is, even though the point estimates are systematically closer to zero, the confidence intervals are too narrow and therefore do not necessarily include the zero too often. In contrast, the coverage rates were often poor for the GLM, especially in scenarios with larger product term coefficients. That is, the confidence intervals were often too narrow to include the true product term parameter, leading to coverage rates down to 19.9%. The LV-CRM, however, yielded accurate coverage rates under all conditions.

Limitations and extensions

The partial derivative framework by Kim and McCabe ( 2022 ) offers a modern technique to quantify interaction effects in non-linear models, but the adaption of this framework to latent variable models can be challenging. In the empirical example, we used this framework to report interaction effects at the means of the covariates and at selected points of the latent variables. However, estimating individual interaction effects and then computing aggregates of them (e.g., average interaction effect) is not straightforward for latent variable models because the individual values of the latent variables are unobserved. Computing an average interaction effect would in this case require a mixed averaging procedure (i.e., sample average for observed predictors and integration techniques for latent predictors) or additional distributional assumptions for the observed predictors. Thus, extending the partial derivative framework for latent variable models is an important task for future research.

Wald test-based statistical inferences for estimated parameters in a SEM (e.g., for a product term coefficient) are not invariant against different methods of scaling the latent variables (Gonzalez & Griffin, 20001 ). In both the simulations studies and the empirical example, we identified the latent variables as standard normal scales, which performed well in terms of power and efficiency in previous studies (Klopp, & Klößner, 2021 ). Additionally, in the simulation studies, the data-generating model exactly matched our identification method. In the empirical example, however, a different scaling method would affect the standard errors of the estimated parameters and, thus, also the interaction effects, which are functions of these. While single parameters can alternatively be tested with a likelihood-ratio test (Gonzalez & Griffin, 20001 ), we are not aware of an alternative for the interaction effects. Thus, critically reflecting the choice of scale and comparing results between scaling methods seems warranted.

A well-known limitation of the marginal maximum likelihood approach is the computational burden, which becomes unfeasible if multiple latent variables ( \(\ge 3\) ) are involved. However, there exist different techniques to alleviate the computational burden (see Skrondal & Rabe-Hesketh, 2004 , Ch. 6, for an accessible overview): First, numerically efficient techniques from the family of Gauss–Hermite quadratures can be used for normally distributed latent variables. Here, the integration points and weights are derived through rule-based computations. Exponential growth of the number of integration points can be drastically reduced with techniques like adaptive Gauss–Hermite quadrature or Laplace approximation. An alternative can be the use of sparse grids (Heiss & Winschel, 2008 ), where integration points are removed if their weight falls below a certain cutoff point, resulting in a considerably smaller grid. Second, Monte Carlo integration offers an alternative for high-dimensional integration problems as well as in situations with non-normal latent variables. Here, the integration points and weights are randomly drawn from the target distribution. In contrast to Gauss–Hermite techniques, the number of integration points does not necessarily grow exponentially and the weights are always equal to 1. Especially for non-normally distributed latent variables, this technique can be facilitated with a Gauss–Hermite rule-based importance sampling approach (Elvira et al., 2021 ). Third, in some cases, it is possible to reduce the dimension of numerical integration below the number of latent variables. Rockwood ( 2021 ) illustrates this in an example with five latent dimensions, where one dimension of integration suffices after a re-parameterization of the model. While this reduction technique does not generalize directly to a model with interaction terms, it can be useful in situations where only few of the latent variables are actually involved in interactions.

While the LV-CRM is an extension of both the G-SEM framework (Rockwood, 2021 ) and the NB-MG-SEM framework (Kiefer & Mayer, 2021a , b ), it is more restrictive as these frameworks in some regards. The LV-CRM can in principle be extended to be a full generalization of the G-SEM framework. Such a generalization would include multiple outcome variables, a structural model among the latent variables, and allow for manifest covariates in the measurement model. Especially the possibility for multiple outcomes would allow for more complex regression models, for example, zero-inflated count regression models. In addition, a G-SEM model also allows for non-linear measurement models, that is, item response theory (IRT) models for categorical and count outcomes can be adapted for the measurement model (Rasch, 1960 ). Vice versa, modern IRT models for count models could be adapted to provide more versatile count regression models (Beisemann, 2022 ; Forthmann et al., 2020 ).

The two main differences (besides the latent interactions) between the LV-CRM and the NB-MG-SEM are: First, the NB-MG-SEM is based on a multigroup framework which allows for more modeling flexibility when it comes to group-specific effects. For example, it is possible to estimate group-specific overdispersion parameters or measurement error variances. Thus, it offers an alternative to model heterogeneity in parameters. Second, in the LV-CRM, we distinguished between stochastic and fixed observed variables. That is, manifest predictors in the LV-CRM are considered as fixed by design. The NB-MG-SEM models all observed variables as being stochastic (i.e., randomly sampled). While this distinction should have no effect on the estimation of the regression coefficients (Kiefer & Mayer, 2019 ), the stochastic approach additionally estimates various moments (i.e., expectation, variance, covariance) of the manifest predictors, which can be used for further analyses.

Availability of data and materials

The datasets generated during and/or analyzed during the simulation studies of the current study are available in the OSF repository, https://osf.io/q7knc The dataset analyzed for the empirical example of the current study are not publicly available due to data privacy laws. Because of this limitation, analysis code is illustrated with a synthetic dataset, which is also available from the OSF repository.

Code availability

Materials and analysis code are available at https://osf.io/q7knc .

It is possible to allow for quadratic terms of the latent variables (e.g., \(\eta _k^2\) ) by changing the starting index of the second sum from \(k+1\) to k .

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New research uncovers how different types of love activate the brain

The image shows brain areas that are particularly activated by the types of love perceived as closest.

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Researchers at Aalto University have made significant strides in understanding how different forms of love activate specific areas of the brain. Their study, which explored the neurological responses to six distinct types of love, reveals that love for close relationships, such as that between a parent and child, strongly engages the brain's reward system and areas associated with social processing. The study involved 55 parents in loving relationships,

who were exposed to short narratives designed to evoke various forms of love while their brain activity was measured using functional magnetic resonance imaging (fMRI). The results showed that parental love was the most intense of all the types examined. Other forms of love studied included romantic love, love for friends, love for strangers, love for pets, and love for nature. The findings have been published in the journal Cerebral Cortex by Oxford University Press.

“We provide a more comprehensive view of brain activity associated with different types of love than previous studies. Love activates patterns in the brain’s reward system, medial frontal cortex, lateral occipital areas, and precuneus,” explained Dr. Pärttyli Rinne , the philosopher and researcher who coordinated the study.

Love's Impact on the Brain

The research team, which included scientists Juha Lahnakoski , Heini Saarimäk i, Mikke Tavast , Mikko Sams , and Linda Henriksson , noted that despite love being a fundamental human experience, it has been surprisingly under-researched in the context of brain imaging. Professor Sams highlighted the importance of this research, suggesting that a better understanding of love’s neural underpinnings could contribute to addressing mental health issues such as attachment disorders, depression, and relationship challenges.

“One intriguing aspect of our findings is how similar the brain areas activated by different types of interpersonal love are. The differences lie primarily in the intensity of activation, with all forms of interpersonal love engaging social brain areas. In contrast, love for pets or nature activates different regions,” Rinne said.

The study also uncovered unique patterns in the brain’s response to love for pets. Pet owners showed heightened activity in social brain areas when imagining scenarios involving their pets, compared to those who do not own pets. This difference was particularly noticeable in a scenario where participants imagined their pet curling up next to them on the couch, highlighting the special bond between pets and their owners.

Implications for Understanding Love

The research not only advances scientific knowledge but also offers insights into why the term "love" is used in such diverse contexts, from romantic love and sexual desire to parental affection and compassionate love.

“Our new findings can help explain why love is so multifaceted and why we use the word in so many different contexts,” Rinne added.

By controlling the study with neutral scenarios, such as brushing teeth or staring out of a bus window, the researchers ensured that the brain activity measured was specifically related to the experience of love. Interestingly, even when participants were asked to simply imagine a loving scenario, the brain’s reward system remained significantly activated, particularly in the case of parental love.

The study's results could pave the way for further research into the complex emotions surrounding love and their impact on the brain, potentially informing future psychological and philosophical discussions on the nature of love and human relationships.

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It is a scientific investigation followed by various methods and techniques. "D. Slesinger and D. Stephenson define social science research as the manipulation of things, concepts, or symbols to generalize to extend, correct, or verify knowledge whether that knowledge aids in the construction of theory or the practice of an art".
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There are different types of research used in the social sciences. However, applied, explanatory, exploratory, and evaluation approaches are some of the more popular approaches social scientists use. A research process that consists of different stages and the nature and characteristics of social research are also discussed in this chapter.
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Social Research: Definition. Social Research is a method used by social scientists and researchers to learn about people and societies so that they can design products/services that cater to various needs of the people. Different socio-economic groups belonging to different parts of a county think differently.
PDF Social Research: Definitions, Types, Nature, and Characteristics
Social research is an organized, systematic, and scientific activity to critically investigate, explore, experiment, test, and analyse human society and the patterns and meanings of human behaviour (Henn et al., 2009). May (2011) discusses that most social research is conducted after identifying a problem that is regarded as a concern for society.
Types of Research Designs
In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.
Research in the Social Sciences
Social scientists interpret and analyze human behavior, generally using empirical methods of research. Though original data gathering and analysis are central to social sciences research, researchers also use library and Web sources to--obtain raw data for model building or analysis; locate information about a particular model, theory, or ...
Social Science Research: Principles, Methods and Practices (Revised
Science can be grouped into two broad categories: natural science and social science. Natural science is the science of naturally occurring objects or phenomena, such as light, objects, matter, earth, celestial bodies, or the human body. Natural sciences can be further classified into physical sciences, earth sciences, life sciences, and others.
Types of Research Designs
In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.
Social Sciences
Social scientists interpret and analyze human behavior, generally using empirical methods of research. Though original data gathering and analysis are central to social sciences research, researchers also use library and Web sources to--obtain raw data for model building or analysis; locate information about a particular model, theory, or ...
Organizing Your Social Sciences Research Paper
I. Groups of Research Methods. There are two main groups of research methods in the social sciences: The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences.This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured.
Social Research: Definition, Types and Common Methods
Related: Research Methods in Sociology: Types and Examples Types of social research Researchers may refer to the approach used in their research as a type of research. Here are the four commonly referenced types of research: 1. Primary research Primary research involves gathering new data through the creation of an experiment or study.
Social Science Research: Principles, Methods and Practices (Revised
8. Sampling. Sampling is the statistical process of selecting a subset—called a 'sample'—of a population of interest for the purpose of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviours within specific populations. We cannot study entire ...
(PDF) Exploring Thematic Analysis in Qualitative Research
Qualitative research methods play a crucial role in advancing researc h and . ... thematic analysis has gained widespread recognition and adoption in social sciences, healthcare, education, and ...
Transformative mixed methods research in South Africa: Contributions to
This chapter is relevant to three related gaps. First, despite the wealth of insights emerging from the mixed methods literature, mixed methods studies continue to be underrepresented in the social science literature and, when they are used, they are framed simplistically without much attention to the integration of quantitative and qualitative methods. Second, many social science research ...
Humanities Versus Social Science: Key Differences Explained
A degree in social sciences opens up a wide range of career opportunities. Some examples include: Market research analysts, who use social science research methods to understand consumer behavior and market trends. Social workers, who apply their knowledge of human behavior and social institutions to help individuals and communities
6-Day International Workshop on Research Methods in Social Sciences
The 6-Day International Workshop on Research Methods in Social Sciences organised by the School of Social Sciences and Languages in association with SPARC-Govt of India is a comprehensive event designed to enhance the research capabilities of research scholars from the broad disciplines of Social Sciences and Commerce backgrounds, held from August 26th to August 31st, 2024.
Romancing the stone: DMSE researchers crack magnetic garnet mystery
At the time, the methods for growing thin films differed from today's sophisticated deposition processes, which involve applying nanometer-thin layers of material to a surface in a vacuum chamber. To make garnet thin films, researchers used to heat raw materials like rare-earth and iron oxides until they melted and then let them grow on a ...
Interactions between latent variables in count regression models
Behavior Research Methods - In psychology and the social sciences, researchers often model count outcome variables accounting for latent predictors and their interactions. Even though neglecting...
New research uncovers how different types of love activate the brain
Researchers at Aalto University have made significant strides in understanding how different forms of love activate specific areas of the brain. Their study, which explored the neurological responses to six distinct types of love, reveals that love for close relationships, such as that between a parent and child, strongly engages the brain's reward system and areas associated with social ...

Social Science Research: Meaning, Significance, Process, Examples

Significance of social science research

2.2 Research Methods

Field Research

Sociology in the Real World

Participant Observation

Ethnography

Institutional Ethnography

Sociological Research

Experiments

Secondary Data Analysis

Basic Research Strategies for the Social Sciences: Research Methods

Sage Research Methods Online (SRMO)

Research Methodologies

Research Integrity

Research Methods for the Social Sciences: An Introduction

Formats Available

Table of Contents

Ancillary Material

About the Contributors

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Using Science as Evidence in Public Policy (2012)

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Social Research – Definition, Types and Methods

Social Research: Definition

Types of Social Research

Social Research Methods

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Organizing Your Social Sciences Research Paper: Types of Research Designs

Introduction

General Structure and Writing Style

Causal Design

Cohort Design

Cross-Sectional Design

Descriptive Design

Experimental Design

Exploratory Design

Historical Design

Longitudinal Design

Mixed-Method Design

Observational Design

WashU Libraries

Researching in the Social Sciences

Recommended Research Guides

Types of Sources

Conducting the Literature Review

Organizing Your Social Sciences Research Paper

Importance of a Good Methodology Section

Structure and Writing Style

Writing Tip

Another Writing Tip

Yet Another Writing Tip

The sampling process

Probability sampling

Non-probability sampling

Statistics of sampling

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Humanities Versus Social Science: Key Differences Explained

Table of Contents

Understanding Humanities

Approach in Humanities

Key Areas of Study in Humanities

Career Paths for Humanities Graduates

Exploring Social Science

Methodologies in Social Sciences

Core Disciplines in Social Science

Career Opportunities in Social Science

Comparative Analysis: Humanities vs Social Science

Nature and Focus

Research Methods

Practical Applications

Choosing Between Humanities and Social Science

Skill Sets Developed

Degree Considerations