- Distinguish between ‘science’ and ‘pseudo-science’
- Describe the scientific method and distinguish between positivist, interpretivist and critical applications of the scientific method
- Define what reliability, validity, correlation and causation mean in a quantitative research study
- Differentiate between four kinds of research methods: surveys, experiments, field research, and secondary data or textual analysis
- Describe how complexity science paradigm, models and computational methods may revolutionize the social sciences
- Discuss the various ethical issues and decisions that arise as part of the development of research design
- Comment on the kinds of ethical issues that may arise with accessing and using big data for social science research in private and public institutions
4.0 Introduction to Sociological Research Methodologies
As noted in Module One, Harriet Martineau (1802–1876) was one of the first women sociologists in the 19th century. Particularly innovative was her early work on sociological methodology, How to Observe Manners and Morals (1838). Interestingly, Martineau’s work was published 57 years prior to Emile Durkheim’s book, The Rules of Sociological Method, yet it is Durkheim’s text rather than Martineau’s that is frequently identified as the first text in sociological methodology. In this volume she developed the ground work for a systematic social-scientific approach to studying human behaviour. She recognized that the issues of the researcher — subject relationship would have to be addressed differently in a social science as opposed to a natural science. The observer, or “traveller,” as she put it, needed to respect three criteria to obtain valid research: impartiality, critique, and sympathy. The impartial observer could not allow herself to be “perplexed or disgusted” by foreign practices that she could not personally reconcile herself with. Yet at the same time she saw the goal of sociology to be the fair but critical assessment of the moral status of a culture. In particular, the goal of sociology was to challenge forms of racial, sexual, or class domination in the name of autonomy: the right of every person to be a “self-directing moral being.” Finally, what distinguished the science of social observation from the natural sciences was that the researcher had to have unqualified sympathy for the subjects being studied (Lengermann & Niebrugge, 2007). This later became a central principle of Max Weber’s interpretive sociology, although it is not clear that Weber read Martineau’s work.
A large part of her research in the United States analyzed the situations of contradiction between stated public morality and actual moral practices. For example, she was fascinated with the way that the formal democratic right to free speech enabled slavery abolitionists to hold public meetings, but when the meetings were violently attacked by mobs, the abolitionists and not the mobs were accused of inciting the violence (Zeitlin, 1997). This emphasis on studying contradictions followed from the distinction she drew between morals — society’s collective ideas of permitted and forbidden behaviour — and manners — the actual patterns of social action and association in society. As she realized the difficulty in getting an accurate representation of an entire society based on a limited number of interviews, she developed the idea that one could identify key “Things” experienced by all people — age, gender, illness, death, etc. — and examine how they were experienced differently by a sample of people from different walks of life (Lengermann & Niebrugge, 2007). Martineau’s sociology, therefore, focused on surveying different attitudes toward “Things” and studying the anomalies that emerged when manners toward them contradicted a society’s formal morals.
In the 21st century, sociologists continue to confront the challenge of producing objective, explanatory and predictive knowledge about a social reality that is characterized by complexity, contingency and contradiction. Added to this challenge is the relatively popular and widespread assumption that disciplines within the category of social science produce and communicate knowledge that is nothing more than ‘common sense’ or subjective opinion. But is it? Just exactly what is common sense? Duncan Watts, Physicist, Engineer, Sociologist and Computer Scientist weighs in on these issues with some interesting insights into why such popular assumptions may be misguided. Watts also points to how new and emerging technologies may be revolutionizing knowledge production within the social sciences in ways analogous to how with the telescope and microscope revolutionized knowledge production within the physical and natural sciences.
4.1 The Scientific Method
In the conduct of sociological inquiry, sociologists make use of tried-and-true methods of research, such as experiments, surveys, field research, and textual analysis. But humans and their social interactions are so diverse that they can seem impossible to chart or explain. It might seem that science is about discoveries and chemical reactions or about proving hypotheses about elementary particles right or wrong rather than about exploring the nuances of human behaviour. However, this is exactly why scientific models work for studying human behaviour. A scientific process of research establishes parameters that help make sure results are objective and accurate. Scientific methods provide limitations and boundaries that focus a study and organize its results. This is the case for both positivist quantitative methodologies, which seek to translate observable phenomena into unambiguous numerical data, and interpretive qualitative methodologies, which seek to translate observable phenomena into definable units of meaning. The social scientific method in both cases involves developing and testing theories about the world based on empirical (i.e., observable) evidence. The social scientific method is defined by its commitment to systematic observation of the social world, and it strives to be objective, critical, skeptical, and logical. It involves a series of established steps known as the research cycle.
The philosophical principles that inform the steps outlined in the scientific method and distinguish science from pseudo-science are summarized for you in the following video.
However, just because sociological studies use the scientific method it does not make the results less human. Sociological topics like the causes and conditions of political violence are typically not amenable to the mathematical precision or quantifiable predictions of physics or chemistry. In the field of sociology, results of studies tend to provide people with access to knowledge they did not have before — knowledge of people’s social conditions, knowledge of rituals and beliefs, knowledge of trends and attitudes. Nevertheless, no matter what research approach is used, researchers want to maximize the study’s reliability (how likely research results are to be replicated if the study is reproduced). Reliability increases the likelihood that what is true of one person will be true of all people in a group. Researchers also want to maximize the study’s validity (how well the study measures what it was designed to measure).
A subtopic in the field of political violence would be to examine the sources of homegrown radicalization: What are the conditions under which individuals in Canada move from a state of indifference or moderate concern with political issues to a state in which they are prepared to use violence to pursue political goals? The reliability of a study of radicalization reflects how well the social factors unearthed by the research represent the actual experience of political radicals. Validity ensures that the study’s design accurately examined what it was designed to study. An exploration of an individual’s propensity to plan or engage in violent acts or to go abroad to join a terrorist organization should address those issues and not confuse them with other themes such as why an individual adopts a particular faith or espouses radical political views. As research from the UK and United States has in fact shown, while jihadi terrorists typically identify with an Islamic world view, a well-developed Islamic identity counteracts jihadism. Similarly, research has shown that while it intuitively makes sense that people with radical views would adopt radical means like violence to achieve them, there is in fact no consistent homegrown terrorist profile, meaning that it is not possible to predict whether someone who espouses radical views will move on to committing violent acts (Patel, 2011). To ensure validity, research on political violence should focus on individuals who engage in political violence.
Sociologists use the scientific method not only to collect but to interpret and analyze the data. They deliberately apply scientific logic and objectivity. They are interested in but not attached to the results. Their research work is independent of their own political or social beliefs. This does not mean researchers are not critical. Nor does it mean they do not have their own personalities, preferences, and opinions. But sociologists deliberately use the scientific method to maintain as much objectivity, focus, and consistency as possible in a particular study. With its systematic approach, the scientific method has proven useful in shaping sociological studies. The scientific method provides a systematic, organized series of steps that help ensure objectivity and consistency in exploring a social problem. These steps provide the means for accuracy, reliability, and validity. In the end, the scientific method provides a shared basis for discussion and analysis (Merton, 1949/1968). Typically, the scientific method starts with these steps, which are described below: 1) ask a question; 2) research existing sources; and 3) formulate a hypothesis.
4.1.1 Ask a Question
The first step of the scientific method is to ask a question, describe a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geography and time frame. “Are societies capable of sustained happiness?” would be too vague. The question should also be broad enough to have universal merit. “What do personal hygiene habits reveal about the values of students at XYZ High School?” would be too narrow. That said, happiness and hygiene are worthy topics to study.
Sociologists do not rule out any topic, but would strive to frame these questions in better research terms. That is why sociologists are careful to define their terms. As indicated in the previous video, Karl Popper (1902-1994) described the formulation of scientific propositions in terms of the concept of falsifiability (1963). He argued that the key demarcation between scientific and non-scientific propositions was not ultimately their truth, nor their empirical verification, but whether or not they were stated in such a way as to be falsifiable; that is, whether a possible empirical observation could prove them wrong. If one claimed that evil spirits were the source of criminal behaviour, this would not be a scientific proposition because there is no possible way to definitively disprove it. Evil spirits cannot be observed. However, if one claimed that higher unemployment rates are the source of higher crime rates, this would be a scientific proposition because it is theoretically possible to find an instance where unemployment rates were not correlated to crime rates. As Popper said, “statements or systems of statements, in order to be ranked as scientific, must be capable of conflicting with possible, or conceivable, observations” (1963).
Once a proposition is formulated in a way that would permit it to be falsified, the variables to be observed need to be operationalized. In a hygiene study, for instance, hygiene could be defined as “personal habits to maintain physical appearance (as opposed to health),” and a researcher might ask, “How do differing personal hygiene habits reflect the cultural value placed on appearance?” When forming these basic research questions, sociologists develop an operational definition; that is, they define the concept in terms of the physical or concrete steps it takes to objectively measure it. The concept is translated into an observable variable, a measure that has different values. The operational definition identifies an observable condition of the concept.
By operationalizing a variable of the concept, all researchers can collect data in a systematic or replicable manner. The operational definition must be valid in the sense that it is an appropriate and meaningful measure of the concept being studied. It must also be reliable, meaning that results will be close to uniform when tested on more than one person. For example, good drivers might be defined in many ways: Those who use their turn signals; those who do not speed; or those who courteously allow others to merge. But these driving behaviours could be interpreted differently by different researchers, so they could be difficult to measure. Alternatively, “a driver who has never received a traffic violation” is a specific description that will lead researchers to obtain the same information, so it is an effective operational definition. Asking the question, “how many traffic violations has a driver received?” turns the concepts of “good drivers” and “bad drivers” into variables which might be measured by the number of traffic violations a driver has received. Of course the sociologist has to be wary of the way the variables are operationalized. In this example we know that black drivers are subject to much higher levels of police scrutiny than white drivers, so the number of traffic violations a driver has received might reflect less on their driving ability and more on the crime of “driving while black.”
4.1.2 Research Existing Sources
The next step researchers undertake is to conduct background research through a literature review, which is a review of any existing similar or related studies. A visit to a university library and a systematic of credible online databases will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted on the topic at hand and enables them to position their own research to build on prior knowledge. It allows them to sharpen the focus of their research question and avoid duplicating previous research. Researchers — including student researchers — are responsible for correctly citing existing sources they use in a study or sources that inform their work. While it is fine to build on previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized. To study hygiene and its value in a particular society, a researcher might sort through existing research and unearth studies about childrearing, vanity, obsessive-compulsive behaviours, and cultural attitudes toward beauty. It is important to sift through this information and determine what is relevant. Using existing sources educates a researcher and helps refine and improve a study’s design. In the video, “Tips for Writing a Literature Review” you are introduced to the two primary questions that researchers use to focus their review of the literature that is relevant to their research question.
4.1.3 Formulate a Hypothesis
A hypothesis is an informed prediction about how two or more variables are related; it makes a conjectural statement about the relationship between those variables. It is an educated guess because it is not random but based on theory, observations, patterns of experience, or the existing literature. The hypothesis formulates this best guess in the form of a testable proposition. However, how the hypothesis is handled differs between the positivist and interpretive approaches. Positivist methodologies are often referred to as hypothetico-deductive methodologies. A hypothesis is derived from a theoretical proposition. On the basis of the hypothesis a prediction or generalization is logically deduced. In positivist sociology, the hypothesis predicts how one variable influences another. How does being a black driver affect the number of times the police will pull you over?
Successful prediction will determine the adequacy of the hypothesis and thereby test the theoretical proposition. Typically positivist approaches operationalize variables as quantitative data; that is, by translating a social phenomenon like health into a quantifiable or numerically measurable variable like “number of visits to the hospital.” This permits sociologists to formulate their predictions using mathematical language like regression formulas to present research findings in graphs and tables, and to perform mathematical or statistical techniques to demonstrate the validity of relationships.
Variables are examined to see if there is a correlation between them. When a change in one variable coincides with a change in another variable there is a correlation. This does not necessarily indicate that changes in one variable causes a change in another variable, however; just that they are associated. A key distinction here is between independent and dependent variables. In research, independent variables are the cause of the change. The dependent variable is the effect, or thing that is changed. For example, in a basic study, the researcher would establish one form of human behaviour as the independent variable and observe the influence it has on a dependent variable. How does gender (the independent variable) affect rate of income (the dependent variable)? How does one’s religion (the independent variable) affect family size (the dependent variable)? How is social class (the dependent variable) affected by level of education (the independent variable)?
For it to become possible to speak about causation, three criteria must be satisfied:
- There must be a relationship or correlation between the independent and dependent variables.
- The independent variable must be prior to the dependent variable.
- There must be no other intervening variable responsible for the causal relationship.
It is important to note that while there has to be a correlation between variables for there to be a causal relationship, correlation does not necessarily imply causation. The relationship between variables can be the product of a third intervening variable that is independently related to both. For example, there might be a positive relationship between wearing bikinis and eating ice cream, but wearing bikinis does not cause eating ice cream. It is more likely that the heat of summertime causes both an increase in bikini wearing and an increase in the consumption of ice cream.
The distinction between causation and correlation can have significant consequences. For example, Indigenous Canadians are overrepresented in prisons and arrest statistics. In 2013, Indigenous people accounted for about 4 percent of the Canadian population, but they made up 23.2 percent of the federal penitentiary population (Correctional Investigator Canada, 2013). There is a positive correlation between being an Indigenous person in Canada and being in jail. Is this because Indigenous people are racially or biologically predisposed to crime? No. In fact there are at least four intervening variables that explain the higher incarceration of Indigenous people (Hartnagel, 2004): Indigenous people are disproportionately poor and poverty is associated with higher arrest and incarceration rates; Indigenous lawbreakers tend to commit more detectable “street” crimes than the less detectable “white collar” crimes of other segments of the population; the criminal justice system disproportionately profiles and discriminates against Indigenous people; and the legacy of colonization has disrupted and weakened traditional sources of social control in Indigenous communities.
|The greater the availability of affordable housing, the lower the homeless rate.||Affordable Housing||Homeless Rate|
|The greater the availability of math tutoring, the higher the math grades.||Math Tutoring||Math Grades|
|The greater the police patrol presence, the safer the neighbourhood.||Police Patrol Presence||Safer Neighbourhood|
|The greater the factory lighting, the higher the productivity.||Factory Lighting||Productivity|
|The greater the amount of public auditing, the lower the amount of political dishonesty.||Auditing||Political Dishonesty|
A researcher’s operational definitions allow for the measurement of the variables. In a study asking how tutoring improves grades, for instance, one researcher might define “good” grades as a C or better, while another uses a B+ as a starting point for good. Another operational definition might describe “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” Those definitions set limits and establish cut-off points, ensuring consistency and replicability in a study. As the above chart shows, an independent variable is the one that causes a dependent variable to change. For example, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Or rephrased, a child’s sense of self-esteem depends, in part, on the quality and availability of hygienic resources.
Of course, this hypothesis can also work the other way around. Perhaps a sociologist believes that increasing a child’s sense of self-esteem (the independent variable) will automatically increase or improve habits of hygiene (now the dependent variable). Clearly identifying the independent and dependent variables in your study is very important. As the hygiene example shows, simply identifying two topics, or variables, is not enough: Their prospective relationship must be part of the hypothesis. Just because a sociologist forms an educated prediction of a study’s outcome doesn’t mean data contradicting the hypothesis are not welcome. Sociologists analyze general patterns in response to a study, but they are equally interested in exceptions to patterns.
In a study of education, a researcher might predict that high school dropouts have a hard time finding a rewarding career. While it has become at least a cultural assumption that the higher the education, the higher the salary and degree of career happiness, there are certainly exceptions. People with little education have had stunning careers, and people with advanced degrees have had trouble finding work. A sociologist prepares a hypothesis knowing that results will vary.
Hypothesis Formation in Qualitative Research
While many sociologists rely on the positivist hypothetico-deductive method in their research, others operate from an interpretive approach. While still systematic, this approach typically does not follow the hypothesis-testing model that seeks to make generalizable predictions from quantitative variables. Instead, an interpretive framework seeks to understand social worlds from the point of view of participants, leading to in-depth knowledge. It focuses on qualitative data, or the meanings that guide people’s behaviour. Rather than relying on quantitative instruments, like fixed questionnaires or experiments, which can be artificial, the interpretive approach attempts to find ways to get closer to the informants’ lived experience and perceptions.
Interpretive research is generally more descriptive or narrative in its findings than positivist research. It can begin from a deductive approach by deriving a hypothesis from theory and then seeking to confirm it through methodologies like in-depth interviews. However, it is ideally suited to an inductive approach in which the hypothesis emerges only after a substantial period of direct observation or interaction with subjects. This type of approach is exploratory in that the researcher also learns as he or she proceeds, sometimes adjusting the research methods or processes midway to respond to new insights and findings as they evolve.
For example, Glaser and Strauss’ (1967) classic elaboration of grounded theory proposed that qualitative researchers working with rich sources of qualitative data from interviews or ethnographic observations need to go through several stages of coding the data before a strong theory of the social phenomenon under investigation can emerge. In the initial stage, the researcher is simply trying to categorize and sort the data. The researchers do not predetermine what the relevant categories of the social experience are but analyze carefully what their subjects actually say. For example, what are the working definitions of health and illness that hospital patients use to describe their situation? In the first stage, the researcher tries to label the common themes emerging from the data: different ways of describing health and illness. In the second stage, the researcher takes a more analytical approach by organizing the data into a few key themes: perhaps the key assumptions that lay people make about the physiological mechanisms of the body, or the metaphors they use to describe their relationship to illness (e.g., a battle, a punishment, a message, etc.). In the third stage, the researcher would return to the interview subjects with a new set of questions that would seek to either affirm, modify, or discard the analytical themes derived from the initial coding of the interviews. This process can then be repeated until a thoroughly grounded theory is ready to be proposed. At every stage of the research, the researchers are obliged to follow the emerging data by revising their conceptions as new material is gathered, contradictions accounted for, commonalities categorized, and themes re-examined with further interviews.
Application of the scientific method and the different types of empirical evidence that are required within different schools of sociological inquiry are described further in the YouTube video, “Sociology and the Scientific Method: Crash Course Sociology”.
Once the preliminary work is done and the hypothesis defined, it is time for the next research steps: choosing a research methodology, conducting a study, and drawing conclusions. These research steps are discussed below.
4.2 Research Methods
Sociologists examine the world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study — perhaps a positivist, quantitative method for conducting research and obtaining data, or perhaps an ethnographic study utilizing an interpretive framework. Planning the research design is a key step in any sociological study. 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 that they are being observed. A researcher would not stroll into a crime-ridden neighbourhood at midnight, calling out, “Any gang members around?” And if a researcher walked into a coffee shop and told the employees they would be observed as part of a study on work efficiency, the self-conscious, intimidated baristas might not behave naturally. The unique nature of human research subjects is that they can react to the researcher and change their behaviour under observation. As described in the YouTube video, “Sociology and the Scientific Method” the reactivity of human research subjects to the presence of researchers has come to be known as the Hawthorne effect.
In planning a study’s design, sociologists generally choose from four widely used methods of social investigation: survey, experiment, field research, and textual or secondary data analysis (or use of existing sources). Every research method comes with pluses and minuses, and the topic of study, research question and conceptual framework that informs the research strongly influence which method or methods are put to use. Additionally, it is important to acknowledge that different different methods of data collection will require different methods of data analysis. Qualitative methods of data collection produce data or empirical evidence that is rich in meaning and generally descriptive (e.g., narrative, image, etc.). Consequently, these methods of data collection draw on the analytical procedures and interpretive strategies that are used in the humanities and arts. Alternatively, quantitative methods of data collection produce empirical evidence that is represented in a numerical form that allows researchers to draw on the analytical procedures and interpretive strategies that are used in the fields of mathematics and statistics. As quantitative research methods of data collection and analysis are a primary focus in this course, it is informative to reflect on what statistics are and the kinds of roles they play in the conduct of sociological inquiry.
As a research method, a survey collects data from subjects who respond to a series of questions about behaviours and opinions, often in the form of a written questionnaire. The survey is one of the most widely used sociological research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.
At some point or another, everyone responds to some type of survey. The Statistics Canada census is an excellent example of a large-scale survey intended to gather sociological data. Customers also fill out questionnaires at stores or promotional events, responding to questions such as “How did you hear about the event?” and “Were the staff helpful?” You have probably picked up the phone and heard a caller ask you to participate in a political poll or similar type of survey: “Do you eat hot dogs? If yes, how many per month?” Not all surveys would be considered sociological research. Marketing polls help companies refine marketing goals and strategies; they are generally not conducted as part of a scientific study, meaning they are not designed to test a hypothesis or to contribute knowledge to the field of sociology. The results are not published in a refereed scholarly journal where design, methodology, results, and analyses are vetted.
Often, polls on TV do not reflect a general population, but are merely answers from a specific show’s audience. Polls conducted by programs such as American Idol or Canadian Idol represent the opinions of fans but are not particularly scientific. A good contrast to these are the Bureau of Broadcast Measurement (BBM) (now called Numeris) ratings, which determine the popularity of radio and television programming in Canada through scientific market research. Their researchers ask a large random sample of Canadians, age 12 and over, to fill out a television or radio diary for one week, noting the times and the broadcasters they listened to or viewed. Based on this methodology they are able to generate an accurate account of media consumers preferences, which are used to provide broadcast ratings for radio and television stations and define the characteristics of their core audiences.
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 and think — or at least how they say they feel and think. Surveys can track attitudes and opinions, political preferences, individual behaviours such as sleeping, driving, dietary, or texting habits, or factual 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 university 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: That is, 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. According to the laws of probability, random samples represent the population as a whole. The larger the sample size, the more accurate the results will be in characterizing the population being studied. For practical purposes, however, a sample size of 1,500 people will give acceptably accurate results even if the population being researched was the entire adult population of Canada. For instance, an Ipsos Reid poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 1,500 or 10,000 people.
Typically surveys will include a figure that gives the margin of error of the survey results. Based on probabilities, this will give a range of values within which the true value of the population characteristic will be. This figure also depends on the size of a sample. For example, a political poll based on a sample of 1,500 respondents might state that if an election were called tomorrow the Conservative Party would get 30% of the vote plus or minus 2.5% based on a confidence interval of 95%. That is, there is a 5% chance that the true vote would fall outside of the range of 27.5% to 32.5%, or 1 time out of 20 if you were to conduct the poll 20 times. If the poll was based on a sample of 1,000 respondents, the margin of error would be higher, plus or minus 3.1%. This is significant, of course, because if the Conservatives are polling at 30% and the Liberals are polling at 28% the poll would be inconclusive about which party is actually ahead with regard to actual voter preferences.
Problems with accuracy or validity can result if sample sizes are too small because there is a stronger chance the sample size will not capture the actual distribution of characteristics of the whole population. In small samples the characteristics of specific individuals have a greater chance of influencing the results. The validity of surveys can also be threatened when part of the population is inadvertently excluded from the sample (e.g., telephone surveys that rely on land lines exclude people that use only cell phones) or when there is a low response rate. There is also a question of what exactly is being measured by the survey. In the BC election of 2013, polls found that the NDP had the largest popular support but on election day many people who said they would vote NDP did not actually vote, which resulted in a Liberal majority government.
After selecting subjects, the researcher develops a specific plan to ask a list of standardized questions and record responses. It is important to inform subjects of the nature and purpose of the study upfront. If they agree to participate, researchers thank the subjects and offer them a chance to see the results of the study if they are interested. The researchers present the subjects with an instrument or means of gathering the information. A common instrument is a structured written questionnaire in which subjects answer a series of set questions. For some topics, the researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question.
This kind of quantitative data — research collected in numerical form that can be counted — is easy to tabulate. Just count up the number of “yes” and “no” answers or tabulate the scales of “strongly agree,” “agree,” “disagree,” etc. responses and chart them into percentages. This is also the chief drawback of questionnaires, however: their artificiality. In real life, there are rarely any unambiguously yes or no answers. Questionnaires can also ask more complex questions with more complex answers beyond yes, no, agree, strongly agree, or another option next to a check box. In those cases, the answers are subjective, varying from person to person. How do you plan to use your university education? Why do you follow Justin Bieber on Twitter? Those types of questions require short essay responses, and participants willing to take the time to write those answers will convey personal information about their beliefs, views, and attitudes.
Some topics that reflect internal subjective perspectives are impossible to observe directly. Sometimes they can be sensitive and difficult to discuss honestly in a public forum or with a stranger. People are more likely to share honest answers if they can respond to questions anonymously. This type of information is qualitative data — results that are subjective and often based on what is experienced in a natural setting. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, and some of which may be surprising. The benefit of written opinions, though, is the wealth of material that they provide.
An interview is a one-on-one conversation between the researcher and the subject, and is another way of conducting surveys on a topic. Interviews are similar to the short answer questions on surveys in that the researcher asks subjects a series of questions. They can be quantitative if the questions are standardized and have numerically quantifiable answers: Are you employed? (Yes=0, No=1); On a scale of 1 to 5 how would you describe your level of optimism? They can also be qualitative if 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 did 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. Obviously, a sociological interview is also not supposed to be an interrogation. The researcher will benefit from gaining a subject’s trust by empathizing or commiserating with a subject, and by listening without judgement–an insight highlighted in Martineau’s 1838 publication, How to Observe Manners and Morals. Additional insights into the design and use of surveys for the purpose of research are addressed in the following video.
You have probably tested 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 a theory, your results either validate or falsify 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, perhaps, more data can be recorded in a certain amount of time. In a natural or field-based experiment, the generation of 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, then another particular thing will result.
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. This is similar to pharmaceutical drug trials in which the experimental group is given the test drug and the control group is given a placebo or sugar pill.
A real-life example will help illustrate the experimental process in sociology. Between 1974 and 1979 an experiment was conducted in the small town of Dauphin, Manitoba (the “garden capital of Manitoba”). Each family received a modest monthly guaranteed income — a “mincome” — equivalent to a maximum of 60 percent of the “low-income cut-off figure” (a Statistics Canada measure of poverty, which varies with family size). The income was 50 cents per dollar less for families who had incomes from other sources. Families earning over a certain income level did not receive mincome. Families that were already collecting welfare or unemployment insurance were also excluded. The test families in Dauphin were compared with control groups in other rural Manitoba communities on a range of indicators such as number of hours worked per week, school performance, high school drop out rates, and hospital visits (Forget, 2011). A guaranteed annual income was seen at the time as a less costly, less bureaucratic public alternative for addressing poverty than the existing employment insurance and welfare programs. Today it is an active proposal being considered in Switzerland (Lowrey, 2013).
Intuitively, it seems logical that lack of income is the cause of poverty and poverty-related issues. One of the main concerns, however, was whether a guaranteed income would create a disincentive to work. The concept appears to challenge the principles of the Protestant work ethic as elaborated by Max Weber. The study did find very small decreases in hours worked per week: about 1 percent for men, 3 percent for married women, and 5 percent for unmarried women. Forget (2011) argues this was because the income provided an opportunity for people to spend more time with family and school, especially for young mothers and teenage girls. There were also significant social benefits from the experiment, including better test scores in school, lower high school drop out rates, fewer visits to hospital, fewer accidents and injuries, and fewer mental health issues.
Ironically, due to lack of guaranteed funding (and lack of political interest by the late 1970s), the data and results of the study were not analyzed or published until 2011. The data were archived and sat gathering dust in boxes. The mincome experiment demonstrated the benefits that even a modest guaranteed annual income supplement could have on health and social outcomes in communities. People seem to live healthier lives and get a better education when they do not need to worry about poverty. In her summary of the research, Forget notes that the impact of the income supplement was surprisingly large given that at any one time only about a third of the families were receiving the income and, for some families, the income amount would have been very small. The income benefit was largest for low-income working families, but the research showed that the entire community profited. The improvement in overall health outcomes for the community suggest that a guaranteed income would also result in savings for the public health system.
The Stanford Prison Experiment is perhaps one of the most famous sociological experiments ever conducted. In 1971, 24 healthy, middle-class male university students were selected to take part in a simulated jail environment to examine the effects of social setting and social roles on individual psychology and behaviour. They were randomly divided into 12 guards and 12 prisoners. The prisoner subjects were arrested at home and transported blindfolded to the simulated prison in the basement of the psychology building on the campus of Stanford University. Within a day of arriving the prisoners and the guards began to display signs of trauma and sadism respectively. After some prisoners revolted by blockading themselves in their cells, the guards resorted to using increasingly humiliating and degrading tactics to control the prisoners through psychological manipulation. The experiment had to be abandoned after only six days because the abuse had grown out of hand (Haney, Banks, & Zimbardo, 1973). While the insights into the social dynamics of authoritarianism it generated were fascinating, the Stanford Prison Experiment also serves as an example of the ethical issues that emerge when experimenting on human subjects. Additional insights into the design and use of experiments in the conduct of sociological research inquiry are addressed in the following video.
4.2.3 Field Research
The work of sociology rarely happens in limited, confined spaces. Sociologists seldom study subjects in their own offices or laboratories. 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 without doing a lab experiment or a survey. It is a research method suited to an interpretive approach rather than to positivist approaches. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In fieldwork, the sociologists, rather than the subjects, are the ones out of their element. The researcher interacts with or observes a person or people, gathering 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 a care home, a hospital, airport, mall, or beach resort.
While field research often begins in a specific setting, the study’s purpose is to observe specific behaviours in that setting. Fieldwork is optimal for observing how people behave. It is less useful, however, for developing causal explanations of why they behave that way. From the small size of the groups studied in fieldwork, it is difficult to make predictions or generalizations to a larger population. Similarly, there are difficulties in gaining an objective distance from research subjects. It is difficult to know whether another researcher would see the same things or record the same data. In Sociology 112.3 various types of field research methods of data collection and analysis are examined in greater detail including, participant observation, ethnography and institutional ethnography, and case study research.
Choosing a research methodology depends on a number of factors, including the purpose of the research and the audience for whom the research is intended. If we consider the type of research that might go into producing a government policy document on the effectiveness of safe injection sites for reducing the public health risks of intravenous drug use, we would expect public administrators to want quantitative evidence of high reliability to help them make a policy decision. The most reliable data would come from an experimental or quasi-experimental research model in which a control group can be compared with an experimental group using quantitative measures.
This approach has been used by researchers studying InSite in Vancouver (Marshall et al., 2011; Wood et al., 2006). InSite is a supervised safe-injection site where heroin addicts and other intravenous drug users can go to inject drugs in a safe, clean environment. Clean needles are provided and health care professionals are on hand to intervene in the case of overdoses or other medical emergency. It is a controversial program both because heroin use is against the law (the facility operates through a federal ministerial exemption) and because the heroin users are not obliged to quit using or seek therapy. To assess the effectiveness of the program, researchers compared the risky usage of drugs in populations before and after the opening of the facility and geographically near and distant to the facility. The results from the studies have shown that InSite has reduced both deaths from overdose and risky behaviours, such as the sharing of needles, without increasing the levels of crime associated with drug use and addiction.
On the other hand, if the research question is more exploratory (for example, trying to discern the reasons why individuals in the crack smoking subculture engage in the risky activity of sharing pipes), the more nuanced approach of fieldwork is more appropriate. The research would need to focus on the subcultural context, rituals, and meaning of sharing pipes, and why these phenomena override known health concerns. Graduate student Andrew Ivsins at the University of Victoria studied the practice of sharing pipes among 13 habitual users of crack cocaine in Victoria, B.C. (Ivsins, 2010). He met crack smokers in their typical setting downtown and used an unstructured interview method to try to draw out the informal norms that lead to sharing pipes. One factor he discovered was the bond that formed between friends or intimate partners when they shared a pipe. He also discovered that there was an elaborate subcultural etiquette of pipe use that revolved around the benefit of getting the crack resin smokers left behind. Both of these motives tended to outweigh the recognized health risks of sharing pipes (such as hepatitis) in the decision making of the users. This type of research was valuable in illuminating the unknown subcultural norms of crack use that could still come into play in a harm reduction strategy such as distributing safe crack kits to addicts.
4.2.4 Secondary Data or Textual Analysis
While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data or textual analysis. Secondary data do not result from firsthand research collected from primary sources, but are drawn from the already-completed work of other researchers. Sociologists might study texts written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines from any period in history. Using available information not only saves time and money, but it can 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 behaviour 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 Facebook.
One methodology that sociologists employ with secondary data is content analysis. Content analysis is a quantitative approach to textual research that selects an item of textual content (i.e., a variable) that can be reliably and consistently observed and coded, and surveys the prevalence of that item in a sample of textual output. For example, Gilens (1996) wanted to find out why survey research shows that the American public substantially exaggerates the percentage of African Americans among the poor. He examined whether media representations influence public perceptions and did a content analysis of photographs of poor people in American news magazines. He coded and then systematically recorded incidences of three variables: (1) race: white, black, indeterminate; (2) employed: working, not working; and (3) age. Gilens discovered that not only were African Americans markedly overrepresented in news magazine photographs of poverty, but that the photos also tended to under represent “sympathetic” subgroups of the poor—the elderly and working poor—while over representing less sympathetic groups—unemployed, working age adults. Gilens concluded that by providing a distorted representation of poverty, U.S. news magazines “reinforce negative stereotypes of blacks as mired in poverty and contribute to the belief that poverty is primarily a ‘black problem’” (1996).
Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments, public interest research groups, and global organizations like Statistics Canada, the Canadian Centre for Policy Alternatives, or the World Health Organization publish studies with findings that are useful to sociologists. A public statistic that measures inequality of incomes might be useful for studying who benefited and who lost as a result of the 2008 recession; a demographic profile of different immigrant groups might be compared with data on unemployment to examine the reasons why immigration settlement programs are more effective for some communities than for others. One of the advantages of secondary data is that it is nonreactive (or unobtrusive) research, meaning that it does not include direct contact with subjects and will not alter or influence people’s behaviours. 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 needs to do some legwork to track them down and gain access to records. In some cases there is no way to verify the accuracy of existing data. It is easy, for example, to count how many drunk drivers 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 high school diplomas later. Another problem arises when data are unavailable in the exact form needed or do not include the precise angle the researcher seeks. For example, the salaries paid to professors at universities are often published, but the separate 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 have been teaching.
In his research, sociologist Richard Sennett uses secondary data to shed light on current trends. In The Craftsman (2008), he studied the human desire to perform quality work, from carpentry to computer programming. He studied the line between craftsmanship and skilled manual labour. He also studied changes in attitudes toward craftsmanship that occurred not only during and after the Industrial Revolution, but also in ancient times. Obviously, he could not have firsthand knowledge of periods of ancient history, so he had to rely on secondary data for part of his study.
When conducting secondary data or textual 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, Robert and Helen Lynd gathered research for their book Middletown: A Study in Modern American Culture 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 the truth about small American communities. Today, it is an illustration of 1920s attitudes and values.
|Country||Offence||Number||Percent of total offences||Rate (per 100,000 pop).|
One of the common forms in which one encounters secondary data is the contingency table. A contingency table provides a frequency distribution of at least two variables that allows the researcher to see at a glance how the variables are related. Table 2.3 shows the frequency of different types of firearm crime for Canada and the United States. In this table, the independent variable (the causal variable) is the country, either Canada or the United States. The dependent variable, displayed in the columns, is the frequency of offences that involve firearms in the two countries. This is given as an absolute number (“number”), as a percentage of the total number of crimes in that category (i.e., as a percentage of the total number of homicides, major assaults and robberies; “percent of total offences”), and as rate calculated per 100,000 population (“rate”).To interpret the table, the researcher has to pay attention to what adds up to 100%. In this table we have not been given the complete information in each column, but it is straight forward to recognize that if 33% of the homicides in Canada involved the use of a firearm, another 67% of homicides did not. The table, therefore, does not say that 33% of all firearm crimes in Canada were homicides. From these figures one can also calculate the total number of homicides that took place in Canada in 2012 by a simple ratio: If the 172 homicides that involved firearms represents 33% or 1/3 of all the Canadian homicides, then there were (approximately) 516 homicides in Canada in 2012.
The table suggests that there is a definite correlation between country and firearm-related violent crime. Violent crime in the United States tends to involve firearms much more frequently than violent crime in Canada. With respect to homicides, there were 8,813 homicides involving firearms in the United States in 2012, accounting for 69% of all homicides, while in Canada, firearms accounted for 33% of homicides. The column that gives the rates of firearm violence per 100,000 population allows the researcher to identify a comparison figure that takes into account the different population sizes of the two countries. The rate of firearm-related homicide in the United States was about seven times higher than in Canada in 2012 (0.5 per 100,000 compared to 3.5 per 100,000), firearm-related major assault was about ten times higher (53 per 100,000 compared to 5 per 100,000), and firearm-related robbery was about five times higher (8.9 per 100,000 compared to 45.1 per 100,000).
The question that this data raises is about causation. Why are firearm-related violent crimes so much lower in Canada than in the United States? One key element are the legal restrictions on firearm possession in the two countries. Canadian law requires that an individual has a valid license under the Firearms Act in order to own or possess a firearm or to purchase ammunition. Until 2012, all firearms also had to be registered, but with the repeal of the national gun registry provisions for long guns (rifles and shot guns), currently only hand guns and prohibited weapons (assault weapons, fully automatic firearms, and sawed-off rifles or shotguns) have to be registered. In the United States firearm regulations are state-specific and only a few states place restrictions on the possession of firearms. In 2007, there were 89 firearms for every 100 citizens in the United States, which is the highest rate of gun ownership of any country (Cotter, 2014). Nevertheless, as Canada’s firearm-related homicide rate is higher than several peer countries, most notably Japan and the United Kingdom, variables other than gun control legislation might be a factor.
4.3 Sociological Research in an Era of Complexity Science
Since its’ emergence in the 19th century, positivist and interpretive approaches to sociological inquiry have drawn on the principles and procedures of the traditional scientific method to guide the production of sociological knowledge. However, as noted at the beginning of this module, the particular qualities of the subject of sociological research establish a strong basis for exploring why progress within social science cannot be equated with progress in ‘rocket science’.
Not only are there a variety of theoretical perspectives in sociology, but also a diversity of research methodologies that can be used in studying the social. In large part, the choice of research methodology follows from the choice of the research question. Of course, the choice of the research question itself depends on the same sort of underlying values and decisions about the nature of the world that divide the theoretical perspectives in sociology. In addition, the choice of the research question involves both the character of the social phenomenon being studied and the purpose of the research in the first place.
It is useful to map out the different methodologies in a diagram. We can position them along two axes according to: (a) whether the subject matter or purpose of the research calls for highly reliable findings — consistent between research contexts (high reliability) — or for highly valid and nuanced findings true to the specific social situation under observation (unique observation), and (b) whether the nature of the object of research can be meaningfully operationalized and measured using quantitative techniques (quantitative data) or is better grasped in terms of the texture of social meanings that constitute it (qualitative data). The advantages and disadvantages of the different methodologies are summarized in Table 4.4 below.
|Secondary Data Analysis||
More recently there is growing interest within the domain of the social sciences to explore how new paradigms, models and computational methods are revolutionizing the conduct of research inquiry by making previously invisible dimensions of society and social interaction accessible for observation and analysis. A selection of these new developments are introduced in the video, “Social Complexity Overview”.
4.4 Ethical Concerns
Sociologists conduct studies to shed light on human behaviours. Knowledge is a powerful tool that can be used toward positive change. And while a sociologist’s goal is often simply to uncover knowledge rather than to spur action, many people use sociological studies to help improve people’s lives. In that sense, conducting a sociological study comes with a tremendous amount of responsibility. Like any researchers, sociologists must consider their ethical obligation to avoid harming subjects or groups while conducting their research. The Canadian Sociological Association (CSA), is the major professional organization of sociologists in Canada. The CSA is a great resource for students of sociology as well. You can access their website at the following url, https://www.csa-scs.ca/
The CSA maintains a code of ethics — formal guidelines for conducting sociological research — consisting of principles and ethical standards to be used in the discipline. It also describes procedures for filing, investigating, and resolving complaints of unethical conduct. These are in line with the Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans (2010), which applies to any research with human subjects funded by one of the three federal research agencies — the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC).
Practising sociologists and sociology students have a lot to consider. Some of the guidelines state that researchers must try to be skillful and fair-minded in their work, especially as it relates to their human subjects. Researchers must obtain participants’ informed consent, and they must inform subjects of the responsibilities and risks of research before they agree to participate. During a study, sociologists must ensure the safety of participants and immediately stop work if a subject becomes potentially endangered on any level. Researchers are required to protect the privacy of research participants whenever possible. Even if pressured by authorities, such as police or courts, researchers are not ethically allowed to release confidential information. Researchers must make results available to other sociologists, must make public all sources of financial support, and must not accept funding from any organization that might cause a conflict of interest or seek to influence the research results for its own purposes. The CSA’s ethical considerations shape not only the study but also the publication of results.
Recent opportunities created by ‘big data’ for the revolutionary development of methodologies within the social sciences, however, do not come without significant issues arising in the domain of research ethics. A selection of these concerns are explored in the YouTube video, ” Intro to Big Data: Crash Course Statistics # 38″.
Pioneer German sociologist Max Weber identified another crucial ethical concern. Weber understood that personal values could distort the framework for disclosing study results. While he accepted that some aspects of research design might be influenced by personal values, he declared it was entirely inappropriate to allow personal values to shape the interpretation of the responses. Sociologists, he stated, must establish value neutrality, a practice of remaining impartial, without bias or judgement, during the course of a study and in publishing results (1949). A similar position had been expressed by Harriet Martineau in her 1838 manuscript, How to Observe Manners and Morals, as indicated at the outset of this module. Sociologists are obligated to disclose research findings without omitting or distorting significant data. Value neutrality does not mean having no opinions. It means striving to overcome personal biases, particularly subconscious biases, when analyzing data. It means avoiding skewing data in order to match a predetermined outcome that aligns with a particular agenda, such as a political or moral point of view. Investigators are ethically obligated to report results, even when they contradict personal views, predicted outcomes, or widely accepted beliefs. Is value neutrality possible?
Many sociologists believe it is impossible to set aside personal values and retain complete objectivity. Individuals inevitably see the world from a partial perspective. Their interests are central to the types of topics they choose, the types of questions they ask, the way they frame their research, and the research methodologies they select to pursue it. Moreover, facts, however objective, do not exist in a void. As was argued by Jürgen Habermas (1972) sociological research has built-in interests quite apart from the personal biases of individual researchers. Positivist sociology has an interest in pursuing types of knowledge that are useful for controlling and administering social life. Interpretive sociology has an interest in pursuing types of knowledge that promote greater mutual understanding and the possibility of consensus among members of society. Critical sociology has an interest in types of knowledge that enable emancipation from power relations and forms of domination in society. In Habermas’ view, sociological knowledge is not disinterested knowledge. However, is there any human knowledge that is truly disinterested knowledge, given the close historical ties of military, industry and government to the domains of the physical and natural sciences, biomedical sciences and social sciences? This does not discredit the results of research in general, or of sociological research in particular, but allows readers to take into account the perspective of the research when judging the validity and applicability of its outcomes whatever the discipline.
authoritative knowledge: Knowledge based on the accepted authority of the source.
case study: In-depth analysis of a single event, situation, or individual.
casual observation: Knowledge based on observations without any systematic process for observing or assessing the accuracy of observations.
code of ethics: A set of guidelines that the Canadian Sociological Association has established to foster ethical research and professionally responsible scholarship in sociology.
content analysis: A quantitative approach to textual research that selects an item of textual content that can be reliably and consistently observed and coded, and surveys the prevalence of that item in a sample of textual output.
contingency table: A statistical table that provides a frequency distribution of at least two variables.
control group: An experimental group that is not exposed to the independent variable.
correlation: When a change in one variable coincides with a change in another variable, but does not necessarily indicate causation.
dependent variable: Variable changed by another variable.
empirical evidence: Evidence corroborated by direct experience and/or observation.
ethnography: Observing a complete social setting and all that it entails.
experiment: The testing of a hypothesis under controlled conditions.
field research: Gathering data from a natural environment without doing a lab experiment or a survey.
Hawthorne effect: When study subjects behave in a certain manner due to their awareness of being observed by a researcher.
hypothesis: An educated guess with predicted outcomes about the relationship between two or more variables.
hypothetico-deductive methodologies: Methodologies based on deducing a prediction from a hypothesis and testing the validity of the hypothesis by whether it correctly predicts observations.
independent variable: Variable that causes change in a dependent variable.
inductive approach: Methodologies that derive a general statement from a series of empirical observations.
institutional ethnography: The study of the way everyday life is coordinated through institutional, textually mediated practices.
interpretive approach: A sociological research approach that seeks in-depth understanding of a topic or subject through observation or interaction.
intervening variable: An underlying variable that explains the correlation between two other variables.
interview: A one-on-one conversation between a researcher and a subject.
literature review: A scholarly research step that entails identifying and studying all existing studies on a topic to create a basis for new research.
nonreactive: Unobtrusive research that does not include direct contact with subjects and will not alter or influence people’s behaviours.
operational definitions: Specific explanations of abstract concepts that a researcher plans to study.
overgeneralization: Knowledge that draws general conclusions from limited observations.
participant observation: Immersion by a researcher in a group or social setting in order to make observations from an “insider” perspective.
population: A defined group serving as the subject of a study.
positivist approach: A research approach based on the natural science model of knowledge utilizing a hypothetico-deductive formulation of the research question and quantitative data.
primary data: Data collected directly from firsthand experience.
qualitative data: Information based on interpretations of meaning.
quantitative data: Information from research collected in numerical form that can be counted.
random sample: A study’s participants being randomly selected to serve as a representation of a larger population reliability a measure of a study’s consistency that considers how likely results are to be replicated if a study is reproduced research design a detailed, systematic method for conducting research and obtaining data.
sample: Small, manageable number of subjects that represent the population.
scientific method: A systematic research method that involves asking a question, researching existing sources, forming a hypothesis, designing and conducting a study, and drawing conclusions.
secondary data analysis: Using data collected by others but applying new interpretations.
selective observation: Knowledge based on observations that only confirm what the observer expects or wants to see.
surveys: Data collections from subjects who respond to a series of questions about behaviours and opinions, often in the form of a questionnaire.
textually mediated communication: Institutional forms of communication that rely on written documents, texts, and paperwork.
traditional knowledge: Knowledge based on received beliefs or the way things have always been done.
validity: The degree to which a sociological measure accurately reflects the topic of study.
value neutrality: A practice of remaining impartial, without bias or judgment, during the course of a study and in publishing results.
variable: A characteristic or measure of a social phenomenon that can take different values.
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