Introduction to Sociology/Sociological Methods
|Pat was confused. Choosing a college to attend was an important decision with life-long implications, but it was difficult to know which school was the right one to attend. Pat had applied to and been accepted by several schools, but now was the time to make a commitment. Pat sought out information from various sources to help make the choice, but received different answers. Pat’s parents thought the best choice was to attend the school closest to their home. It was inexpensive and Pat could save money by living at home. Pat’s best friend wanted both of them to attend the same school where they could be roommates, while Pat’s grandparents thought attending college at all was pretty much a waste of time and money.
In spite of these conflicts, Pat continued to think about the recommendations mentioned in an article written by a social scientist, who had carefully examined the life trajectories of large numbers of people who had graduated from various colleges across the U.S. Those recommendations were again completely different than those made by friends and family. With all this different information, how could Pat make the right choice?
The goal of this chapter is to introduce the methods employed by sociologists in their study of social life. This is not a chapter on statistics nor does it detail specific methods in sociological investigation. The primary aim is to illustrate how sociologists go beyond common sense understandings in trying to explain or understand social phenomena.They do not see the world as we normally do, they question and analyze why things happen and if there is a way to stop a problem before it happens.
At issue in this chapter are the methods used by sociologists to claim to speak authoritatively about social life. There are dozens of different ways that human beings claim to acquire knowledge. A few common examples are:
Authority: Choosing to trust another source for information is the act of making that source an authority in your life. Parents, friends, the media, religious leaders, your professor, books, or web pages are all examples of secondary sources of information that some people trust for information.
Experience: People often claim to have learned something through an experience, such as a car accident or using some type of drug. Some physical skills, such as waterskiing or playing basketball, are acquired primarily through experience. On the other hand, some experiences are subjective and are not generalizable to all.
Logic: Simple deduction is often used to discern truth from falsity and is the primary way of knowing used in philosophy. I might suggest that if I fall in a swimming pool full of water, I will get wet. If that premise is true and I fall in a swimming pool, you could deduce that I got wet.
Tradition: Many people who live in societies that have not experienced industrialization decide what to do in the future by repeating what was done in the past. Even in modern societies, many people get satisfaction out of celebrating holidays the same way year after year. Fast-paced change in modern societies, however, makes traditional knowledge less and less helpful in making good choices.
Revelation: Some people claim to acquire knowledge believed to be valid by consulting religious texts and believing what is written in them, such as the Torah, the Bible, the Koran, the Bhagavad Gita, or the Book of Mormon. Others claim to receive revelations from a higher power in the form of voices or a general intuitive sense of what one should do.
Science: The scientific method combines the use of logic with controlled experience, creating a novel way of discovery that marries sensory input with careful thinking. By adopting a model of cause and effect, scientists produce knowledge that can explain certain phenomena and even predict various outcomes before they occur.
These methods of claiming to know certain things are referred to as epistemologies. An epistemology is simply a way of knowing. In Sociology, information gathered through science is privileged over all others. That is, information gleaned using other epistemologies will be rejected if it is not supported by evidence gathered using the scientific method.
The Scientific Method
A scientific method or process is considered fundamental to the scientific investigation and acquisition of new knowledge based upon verifiable evidence. In addition to employing the scientific method in their research, sociologists explore the social world with several different purposes in mind. Like the physical sciences (i.e., chemistry, physics, etc.), sociologists can be and often are interested in predicting outcomes given knowledge of the variables and relationships involved. This approach to doing science is often termed positivism (though perhaps more accurately should be called empiricism). The positivist approach to social science seeks to explain and predict social phenomena, often employing a quantitative approach where aspects of social life are assigned numerical codes and subjected to in-depth analyses to uncover trends often missed by a casual observer. This approach most often makes use of deductive reasoning, which initially forms a theory and hypothesis, which are then subjected to empirical testing.
Unlike the physical sciences, sociology (and other social sciences, like anthropology) also often seek simply to understand social phenomena. Max Weber labeled this approach Verstehen, which is German for understanding. This approach, called qualitative sociology, aims to understand a culture or phenomenon on its own terms rather than trying to develop a theory that allows for prediction. Qualitative sociologists more frequently use inductive reasoning where an investigator will take time to make repeated observations of the phenomena under study, with the hope of coming to a thorough and grounded understanding of what is really going on.
Both approaches employ a scientific method as they make observations and gather data, propose hypotheses, and test or refine their hypotheses in the formulation of theories. These steps are outlined in more detail below.
Sociologists use observations, hypotheses, deductions, and inductions to understand and ultimately develop explanations for social phenomena in the form of theories. Predictions from these theories are tested. If a prediction turns out to be correct, the theory survives. If not, the theory is modified or discarded. The method is commonly taken as the underlying logic of scientific practice. Science is essentially an extremely cautious means of building a supportable, evidenced understanding of our natural and social worlds.
The essential elements of a scientific method are iterations and recursions of the following four steps:
- Characterization (operationalization or quantification, observation and / or measurement)
- Hypothesis (a theoretical, hypothetical explanation of the observations and / or measurements)
- Prediction (logical deduction from the hypothesis or logical induction from the data)
- Testing (informing the validity of the hypothesis by comparing it against carefully gathered, meaningful sensory input)
A scientific method depends upon a careful characterization of the subject of the investigation. While seeking the pertinent properties of the subject, this careful thought may also entail some definitions and observations; the observation often demands careful categorization, measurement and/or counting.
The systematic, careful collection of measurements, counts or categorical distinctions of relevant quantities or qualities is often the critical difference between pseudo-sciences, such as alchemy, and a science, such as chemistry. Scientific measurements are usually tabulated, graphed, or mapped, and statistical manipulations, such as correlation and regression, performed on them. The measurements might be made in a controlled setting, such as a laboratory, or made on more or less inaccessible or unmanipulatable objects such as human populations. The measurements often require specialized scientific instruments such as thermometers, spectroscopes, or voltmeters, and the progress of a scientific field is usually intimately tied to their invention and development. In a similar fashion, categorical distinctions are often outlined, graphed and / or arranged in relation to the variation of qualities found within and between natural settings (mostly) free of manipulation. These categorical distinctions generally require specialized coding or sorting protocols that allow differential qualities to be sorted into distinct categories, which may be compared and contrasted over time, and the progress of scientific fields in this vein are generally tied to the accumulation of systematic categories and observations across multiple natural sites. In both cases, scientific progress relies upon ongoing intermingling between measurement and categorical approaches to data analysis.
Measurements demand the use of operational definitions of relevant quantities (a.k.a. operationalization). That is, a scientific quantity is described or defined by how it is measured, as opposed to some more vague, inexact or idealized definition. The operational definition of a thing often relies on comparisons with standards: the operational definition of mass ultimately relies on the use of an artifact, such as a certain kilogram of platinum kept in a laboratory in France. In short, to operationalize a variable means creating an operational definition for a concept someone intends to measure. Similarly, categorical distinctions rely upon the use of previously observed categorizations. A scientific category is thus described or defined based upon existing information gained from prior observations and patterns in the natural world as opposed to socially constructed "measurements" and "standards" in order to capture potential missing pieces in the logic and definitions of previous studies. In short, to categorize observed patterns scientists must initially reject and / or critique existing operational definitions and standards. In both cases, however, how this is done is very important as it should be done with enough precision that independent researchers should be able to use your description of your measurement or construction of categories, and repeat either or both.
The scientific definition of a term sometimes differs substantially from its natural language usage. For example, sex and gender are often used interchangeably in common discourse, but have distinct meanings in sociology. Scientific quantities are often characterized by their units of measure which can later be described in terms of conventional physical units when communicating the work while scientific categorizations are generally characterized by their shared qualities which can later be described in terms of conventional linguistic patterns of communication.
Measurements and categorizations in scientific work are also usually accompanied by estimates of their uncertainty or disclaimers concerning the scope of initial observations. The uncertainty is often estimated by making repeated measurements of the desired quantity. Uncertainties may also be calculated by consideration of the uncertainties of the individual underlying quantities that are used. Counts of things, such as the number of people in a nation at a particular time, may also have an uncertainty due to limitations of the method used. Counts may only represent a sample of desired quantities, with an uncertainty that depends upon the sampling method used and the number of samples taken (see the central limit theorem). Similarly, the scope of initial observations may be recorded via count and / or qualitative assessments of the nature and method of previous studies, which allows researchers to expand and / or replicate categorizations dependent upon and contextualized via a specific scope of observational data.
A hypothesis includes a suggested explanation of the subject. In quantitative work, it will generally provide a causal explanation or propose some association between two variables. If the hypothesis is a causal explanation, it will involve at least one dependent variable and one independent variable. In qualitative work, hypotheses generally involve potential assumptions built into existing causal statements, which may be examined in a natural setting.
Variables are measurable phenomena whose values or qualities can change (e.g., class status can range from lower- to upper-class). A dependent variable is a variable whose values or qualities are presumed to change as a result of the independent variable. In other words, the value or quality of a dependent variable depends on the value of the independent variable. Of course, this assumes that there is an actual relationship between the two variables. If there is no relationship, then the value or quality of the dependent variable does not depend on the value of the independent variable. An independent variable is a variable whose value or quality is manipulated by the experimenter (or, in the case of non-experimental analysis, changes in the society and is measured or observed systematically). Perhaps an example will help clarify. In a study of the influence of gender (as a value) on promotion, the independent variable would be gender/sex. Promotion would be the dependent variable. Change in promotion is hypothesized to be dependent on gender. Similarly, in a study of gender's (as a quality) relation to promotion, the independent variables are gender/sex and promotion, and the dependent variable is the way people use, discuss, and / or make sense of both sex/gender and promotion in their ongoing activities or narratives.
Scientists use whatever they can — their own creativity, ideas from other fields, induction, deduction, systematic guessing, etc. — to imagine possible explanations for a phenomenon under study. There are no definitive guidelines for the production of new hypotheses. The history of science is filled with stories of scientists claiming a flash of inspiration, or a hunch, which then motivated them to look for evidence to support, refute, or refine their idea or develop an entirely new framework.
A useful quantitative hypothesis will enable predictions, by deductive reasoning, that can be experimentally assessed. If results contradict the predictions, then the hypothesis under examination is incorrect or incomplete and requires either revision or abandonment. If results confirm the predictions, then the hypothesis might be correct but is still subject to further testing. Predictions refer to experimental designs with a currently unknown outcome. A prediction (of an unknown) differs from a consequence (which can already be known). On the other hand, a useful qualitative hypothesis will enable question or critique, by inductive reasoning, of existing and / or taken-for-granted beliefs, assumptions, and theories developed within or beyond scientific settings.
Once a prediction is made, a method is designed to test or critique it. The investigator may seek either confirmation or falsification of the hypothesis, and refinement or understanding of the data. Though a variety of methods are used by both natural and social scientists, laboratory experiments remain one of the most respected methods by which to test hypotheses.
Scientists assume an attitude of openness and accountability on the part of those conducting an experiment. Detailed record keeping is essential, to aid in recording and reporting on the experimental results, and providing evidence of the effectiveness and integrity of the procedure. They will also assist in reproducing the experimental results.
The experiment's integrity should be ascertained by the introduction of a control or by observation of existing controls in natural settings. In experiments where controls are observed rather than introduced, researchers take into account potential variables (e.g., the demographics of the sample and researchers as well as the behaviors of both groups) that could influence the findings without intention. On the other hand, in experiments where a control is introduced, two virtually identical experiments are run, in only one of which the factor being tested is varied. This serves to further isolate any causal phenomena. For example in testing a drug it is important to carefully test that the supposed effect of the drug is produced only by the drug. Doctors may do this with a double-blind study: two virtually identical groups of patients are compared, one of which receives the drug and one of which receives a placebo. Neither the patients nor the doctor know who is getting the real drug, isolating its effects. This type of experiment is often referred to as a true experiment because of its design. It is contrasted with alternative forms below.
Once an experiment is complete, a researcher determines whether the results (or data) gathered are what was predicted or assumed in the literature beforehand. If the experimental conclusions fail to match the predictions/hypothesis and / or existing scientific arguments, then one returns to the failed hypothesis and re-iterates the process - modifying one's theory or developing a new one - or attempts to publish the results as a suggestion for gaps in existing theories or findings. If the experiment appears successful - i.e. fits the hypothesis and existing scientific arguments - the experimenter often will attempt to publish the results so that others (in theory) may reproduce the same experimental results, verifying the findings and / or the existing scientific narrative of the time in the process.
An experiment is not an absolute requirement. In observation based fields of science actual experiments must be designed differently than for the classical laboratory based sciences. Sociologists are more likely to employ quasi-experimental designs where data are collected from people by surveys or interviews, but statistical means are used to create groups that can be compared. For instance, in examining the effects of gender on promotions, sociologists may control for the effects of social class as this variable will likely influence the relationship. Unlike a true experiment where these variables are held constant in a laboratory setting, quantitative sociologists use statistical methods to hold constant social class (or, better stated, partial out the variance accounted for by social class) so they can see the relationship between gender and promotions without the interference of social class.
The four components of research described above are integrated into the following steps of the research process.
- Define the topic/problem: Identify your topic of interest and develop a research question in the form of a cause-and-effect relationship.
- Conduct a review of the literature: Access studies that have already been performed by other researchers and published in peer-reviewed journals. You'll find out what is already known about the topic and where more research is needed.
- Formulate a hypothesis: Refine your research question in a way that will add new information to the existing research literature, expressing it in the form of a testable research hypothesis. This includes identifying two or more variables and articulating how one variable is thought to influence the other.
- Design the research: Decide on a way to approach data collection that will provide a meaningful test of the research hypothesis. Some designs include data collection at only one point in time, but more complex questions require data gathering over time and with different groups of people.
- Select a research method: Once a design has been established, one or more actual data gathering strategies will need to be identified. Each method comes with its own strengths and weaknesses, so sociologists are increasingly incorporating mixed-methods approaches in their research designs to enrich their knowledge of the topic. Some of the more popular research methods used by sociologists are: Surveys or Interviews, Experiments, Unobtrusive measures, and Participant Observation or Field Research
- Operationalize variables: Operationalizing means deciding exactly how each variable of interest will be measured. In survey research, this means deciding on the exact wording of the question or questions used to measure each variable, a listing of all possible responses to closed-ended questions, and a decision as to how to compute variables using multiple indicators.
- Identify the population and draw a sample: A population is the group a researcher is interested in learning about. Is it all students at one particular University? All residents of the United States? All nonprofit organizations in a particular city? Because it is frequently too expensive to try to collect data from all units in a population, a sample of those units is often selected. Samples that use principles of random selection, where every unit in the population has an equal chance of being included in the sample, have the best chance of reflecting the views and behaviors of the entire population of focus.
- Collect the data: Data collection must be systematic and rigorous so that procedural mistakes do not create artificial results.
- Analyze the results: Powerful statistical packages today make data analysis easier than it has ever been. Still, great care needs to be taken to accurately code the data (i.e. transpose responses into numbers), enter it into the computer, and to choose the appropriate statistics to be calculated for analysis.
- Reporting the Results: Research results are shared with the larger community through presentations, reports, and publications in peer-reviewed journals. This allows others to consider the findings, the methods used, and any limitations of the study.
Qualitative sociologists generally employ observational and analytic techniques that allow them to contextualize observed patterns in relation to existing hierarchies or assumptions within natural settings. Using an earlier example, qualitative sociologists examining the experience of gender and promotion may ascertain the existing beliefs about gender and about promotion by the people being studied, official documentation outlining the rules of promotion and / or policies concerning gender within the setting, and variations in the ways people occupying different racial, classed, gendered, sexual, religious, or aged social locations interpret and make sense of both gender and promotion. Since variables (such as social class) cannot be "held constant" or "controlled for" in natural settings, qualitative sociologists explore the potential influence of these factors on actual behaviors in order to refine existing mathematical and / or experimental theories containing assumptions and controls unavailable beyond the laboratory or mathematical software. Thus, while the true experiment is ideally suited for the performance of quantitative science, especially because it is the best quantitative method for deriving causal relationships, other methods of hypothesis testing are commonly employed in the social sciences, and qualitative methods of critique and analysis are utilized to fact check the assumptions and theories created upon the basis of "controlled" (rather than natural) circumstances.
Evaluation and Iteration
The scientific process is iterative. At any stage it is possible that some consideration will lead the scientist to repeat an earlier part of the process. For instance, failure of a hypothesis to produce interesting and testable predictions may lead to reconsideration of the hypothesis or of the definition of the subject. Similarly, advances in qualitative research generally lead to reformulation of quantitative and experimental techniques and assumptions (this relationship also occurs regularly in the other direction where findings from quantitative studies direct qualitative attention to new areas and / or potential relationships).
It is also important to note that science is a social enterprise, and scientific work will become accepted by the community only if it can be verified and it "makes sense" within existing scientific beliefs and assumptions about the world (when new findings complicate these assumptions and beliefs, we generally witness paradigm shifts in science. Crucially, experimental and quantitative results must be reproduced by others within the scientific community while qualitative studies are designed to complicate, advance, and / or call into question these results. All scientific knowledge is in a state of flux, for at any time new evidence could be presented that contradicts a long-held hypothesis, and new perspectives (e.g., the entrance of minority communities into the academy in the past 50 years) may emerge that call existing scientific techniques, assumptions, and beliefs into question. For this reason, scientific journals use a process of peer review, in which scientists' manuscripts are submitted by editors of scientific journals to (usually one to three) fellow (usually anonymous) scientists familiar with the field for evaluation. The referees may or may not recommend publication, publication with suggested modifications, or, sometimes, publication in another journal. This serves to keep the scientific literature free of unscientific work, helps to cut down on obvious errors, and generally otherwise improves the quality and consistency of the scientific literature, but may also lead to the silencing or delay of new and / or controversial scientific findings. Work announced in the popular press before going through this process is generally frowned upon. Sometimes peer review inhibits the circulation of unorthodox work, and at other times may be too permissive. The peer review process is not always successful, but has been very widely adopted by the scientific community.
The reproducibility or replication of quantitative scientific observations, while usually described as being very important in a scientific method, is actually seldom reported, and is in reality often not done. Referees and editors often reject papers purporting only to reproduce some observations as being unoriginal and not containing anything new. Occasionally reports of a failure to reproduce results are published - mostly in cases where controversy exists or a suspicion of fraud develops. The threat of failure to replicate by others (as well as the ongoing qualitative enterprise designed to explore the veracity of quantitative findings in non-controlled settings), however, serves as a very effective deterrent for most quantitative scientists, who will usually replicate their own data several times before attempting to publish.
Sometimes useful observations or phenomena themselves cannot be reproduced (in fact, this is almost always the case in qualitative science spanning physical and social science disciplines). They may be rare, or even unique events. Reproducibility of quantitative observations and replication of experiments is not a guarantee that they are correct or properly understood. Errors can all too often creep into more than one laboratory or pattern of interpretation (mathematical or qualitative) utilized by scientists. As a result, science itself is an ongoing dialogue and debate wherein each finding (new or old) is continuously subject to new testing and / or critique.
Correlation and Causation
In the scientific pursuit of quantitative prediction and explanation, two relationships between variables are often confused: correlation and causation. While these terms are rarely used in qualitative science, they lie at the heart of quantitative methods, and thus constitute a cornerstone of scientific practice. Correlation refers to a relationship between two (or more) variables in which they change together. A correlation can be positive/direct or negative/inverse. A positive correlation means that as one variable increases (e.g., ice cream consumption) the other variable also increases (e.g., crime). A negative correlation is just the opposite; as one variable increases (e.g., socioeconomic status), the other variable decreases (e.g., infant mortality rates).
Causation refers to a relationship between two (or more) variables where one variable causes the other. In order for a variable to cause another, it must meet the following three criteria:
- the variables must be correlated
- change in the independent variable must precede change in the dependent variable in time
- it must be shown that a different (third) variable is not causing the change in the two variables of interest (a.k.a., spurious correlation)
An example may help explain the difference. Ice cream consumption is positively correlated with incidents of crime.
Employing the quantitative method outlined above, the reader should immediately question this relationship and attempt to discover an explanation. It is at this point that a simple yet noteworthy phrase should be introduced: correlation is not causation. If you look back at the three criteria of causation above, you will notice that the relationship between ice cream consumption and crime meets only one of the three criteria (they change together). The real explanation of this relationship is the introduction of a third variable: temperature. Ice cream consumption and crime increase during the summer months. Thus, while these two variables are correlated, ice cream consumption does not cause crime or vice versa. Both variables increase due to the increasing temperatures during the summer months.
It is important to not confound a correlation with a cause/effect relationship. It is often the case that correlations between variables are found but the relationship turns out to be spurious. Clearly understanding the relationship between variables is an important element of the quantitative scientific process.
Quantitative and Qualitative
Like the distinction drawn between positivist sociology and Verstehen sociology, there is - as noted above in the elaboration of general scientific methods - often a distinction drawn between two types of sociological investigation: quantitative and qualitative.
Quantitative methods of sociological research approach social phenomena from the perspective that they can be measured and/or quantified. For instance, social class, following the quantitative approach, can be divided into different groups - upper-, middle-, and lower-class - and can be measured using any of a number of variables or a combination thereof: income, educational attainment, prestige, power, etc. Quantitative sociologists also utilize mathematical models capable of organizing social experiences into a rational order that may provide a necessary foundation for more in depth analyses of the natural world (importantly, this element of quantitative research often provides the initial or potential insights that guide much theoretical and qualitative analyses of patterns observed - numerically or otherwise - beyond the confines of mathematical models). Quantitative sociologists tend to use specific methods of data collection and hypothesis testing, including: experimental designs, surveys, secondary data analysis, and statistical analysis. Further, quantitative sociologists typically believe in the possibility of scientifically demonstrating causation, and typically utilize analytic deduction (e.g., explore existing findings and deduce potential hypotheses that may be tested in new data). Finally, quantitative sociologists generally attempt to utilize mathematical realities (e.g., existing assumptions and rules embedded within statistical practices) to make sense of natural (e.g., the experience of the actual worlds of people) realities.
Qualitative methods of sociological research tend to approach social phenomena from the Verstehen perspective. Rather than attempting to measure or quantify reality via mathematical rules, qualitative sociologists explore variation in the natural world people may see, touch, and experience during their lives. As such, these methods are primarily used to (a) develop a deeper understanding of a particular phenomenon, (b) explore the accuracy or inaccuracy of mathematical models in the world people experience, (c) critique and question the existing assumptions and beliefs of both scientists and other social beings, and (d) refine measurements and controls used by quantitative scientists via insights gleaned from the experiences of actual people. While qualitative methods may be used to propose or explore relationships between variables, these studies typically focus on explicating the realities people experience that lie at the heart or foundation of such relationships rather than focusing on the relationships themselves. Qualitatively oriented sociologists tend to employ different methods of data collection and analysis, including: participant observation, interviews, focus groups, content analysis, visual sociology, and historical comparison. Further, qualitative sociologists typically reject measurement or quantities (essential to quantitative approaches) and the notion or belief in causality (e.g., qualitative sociologists generally argue that since there is no demonstrated possibility of ever exploring all potential variables or influences in one study, causality is always incomplete and beyond empirical means). Finally, qualitative sociologists generally attempt to utilize natural realities (e.g., the experience of the actual worlds of people) to make sense of these natural realities and complicate mathematical assumptions and rules that may lead scientists into misguided findings that lack applicability to the actual worlds people inhabit.
While there are sociologists who employ and encourage the use of only one or the other method, many sociologists see benefits in combining the approaches. They view quantitative and qualitative approaches as complementary. Results from one approach can fill gaps in the other approach. For example, quantitative methods could describe large or general patterns in society while qualitative approaches could help to explain how individuals understand those patterns. Similarly, qualitative patterns in society can reveal missing pieces in the mathematical models of quantitative research while quantitative patterns in society can guide more in-depth analysis of actual patterns in natural settings. In fact, it is useful to note that many of the major advancements in social science have emerged in response to the combination of quantitative and qualitative techniques that collectively created a more systematic picture of probable and actual social conditions and experiences.
Objective vs. Critical vs. Subjective
Sociologists, like all humans, have values, beliefs, and even pre-conceived notions of what they might find in doing their research. Because sociologists are not immune to the desire to change the world, two approaches to sociological investigation have emerged. By far the most common is the objective approach advocated by Max Weber. Weber recognized that social scientists have opinions, but argued against the expression of non-professional or non-scientific opinions in the classroom. Weber took this position for several reasons, but the primary one outlined in his discussion of Science as Vocation is that he believed it is not right for a person in a position of authority (a professor) to force his/her students to accept his/her opinions in order for them to pass the class. Weber did argue that it was acceptable for social scientists to express their opinions outside of the classroom and advocated for social scientists to be involved in politics and other social activism. The objective approach to social science remains popular in sociological research and refereed journals because it refuses to engage social issues at the level of opinions and instead focuses intently on data and theories.
The objective approach is contrasted with the critical approach, which has its roots in Karl Marx's work on economic structures. Anyone familiar with Marxist theory will recognize that Marx went beyond describing society to advocating for change. Marx disliked capitalism and his analysis of that economic system included the call for change. This approach to sociology is often referred to today as critical sociology (see also action research). Some sociological journals focus on critical sociology and some sociological approaches are inherently critical (e.g., feminism, black feminist thought).
Building on these early insights, the rise of Feminist methods and theories in the 1970's ushered in an ongoing debate concerning critical versus objective realities. Drawing on early Feminist writings by social advocates including but not limited to Elizabeth Cady Stanton, Alice Paul, Ida Wells Barnett, Betty Friedan, and sociological theorists including but not limited to Dorothy Smith, Joan Acker, and Patricia Yancey Martin, Feminist sociologists critiqued "objective" traditions as unrealistic and unscientific in practice. Specifically, they - along with critical theorists like Michel Foucault, bell hooks, and Patricia Hill Collins - argued that since all science was conducted and all data was interpreted by human beings and all human beings have beliefs, values, and biases that they are often unaware of and that shape their perception of reality (see The Social Construction of Reality), objectivity only existed within the beliefs and values of the people that claimed it. Stated another way, since human beings are responsible for scientific knowledge despite the fact that human beings cannot be aware of all the potential biases, beliefs, and values they use to do their science, select their topics, construct measurements, and interpret data, "objective" or "value free" science are not possible. Rather, these theorists argued that the "personal is political" (e.g., our personal decisions - no matter how small - are ultimately influenced by the political context of our lives and ultimately will shape the personal and political realities of others whether or not we are aware of these consequences). As a result, every scientist - regardless of their intentions and / or awareness - may seek to follow Weber's advice concerning objective teaching and research, but must also remain aware that they will ultimately fail to achieve this ideal. Whether or not scientists explicitly invoke their personal opinions in their teaching and research, every decision scientists make will ultimately rely upon - and thus demonstrate to varying degrees - their subjective realities. As a result, current debates typically center around objective (an ideal) versus subjective (data-based) interpretations of science while scholars continue to debate the merits and limitations of subjective/objective versus critical approaches.
Some examples of the subjective basis of both "objective" and "critical" sociology may illustrate the point. First, we may examine the research process for both objective and critical sociologists while paying attention to the many decisions people must make to engage in any study from either perspective. These decisions include:
- The selection of a research topic (this selection reveals something the author believes is important whether or not it is)
- The selection of data (this selection reveals data the author believes is reliable whether or not it is)
- If the researcher decides to collect their own data, then they must:
- Decide where to collect data
- Decide who to collect data from
- Decide what questions to ask (which ones they believe will answer the question) and how to ask these questions (which forms of talk they believe are best for getting the answers they want)
- Decide how much data to collect
- Decide how to analyze the data collected (if mathematically, which protocols will be used and which software program, and if qualitatively which themes will ze look for and / or what software program)
- Decide how to measure or categorize the data (if mathematically, what set of parameters counts as a good measure, and if qualitatively what must a category contain)
- Decide how to interpret the measurements or categories (if mathematically, what exactly do the numbers mean socially, and if qualitatively what do the categories say about society)
- Decide how to discuss the interpretation (which theories should be used and which ones should be ignored)
- If the researcher decides to use secondary data, this becomes even more complicated. While they will have to do the final four items listed above, they must also:
- Trust that the data collection occurred properly
- Trust that the data was organized properly
- Trust that the questions were answered properly
- Trust that the sample is appropriate
As you can see above, the research process itself is full of decisions that each researcher must make. As a result, researchers themselves have no opportunity to conduct objective studies because doing research requires them to use their personal experiences and opinions (whether these arise from personal life, the advice of the people that taught them research methods, or the books they have read that were ultimately subject to the same subjective processes) throughout the process. As a result, researchers can - as Feminists have long argued - attempt to be as objective as possible, but never actually hope to reach objectivity. This same problem arises in Weber's initial description of teaching. For someone to teach any course, for example, they must make a series of decisions including but not limited to:
- Deciding what subjects to cover within the overall course
- Deciding which readings to use to convey information
- Deciding what measures of learning will be used and what measures will be left out of the course
- Deciding what counts as an appropriate or inappropriate answer on any and all measures used in the course
As a result, Weber's objectivity dissolves before the teacher ever enters the classroom. Whether or not the teacher (or researcher) explicitly takes a political, religious, or social stance, he or she will ultimately demonstrate personal stances, beliefs, values, and biases implicitly throughout the course.
Although the recognition of all science as ultimately subjective to varying degrees is fairly well established at this point, the question of whether or not scientists should embrace this subjectivity remains an open one (e.g., to be or not to be political in classrooms and research projects). Further, there are many scientists (in sociology and other sciences) that still cling to beliefs about objectivity, and thus promote this belief (political in and of itself) in their teaching, research, and peer review. As a result, the debate within the field continues without resolution, and will likely be an important part of scientific knowledge and scholarship for some time to come.
Ethical considerations are of particular importance to sociologists because of the subject of investigation - people. Because ethical considerations are of so much importance, sociologists adhere to a rigorous set of ethical guidelines. The most important ethical consideration of sociological research is that participants in sociological investigation are not harmed. While exactly what this entails can vary from study to study, there are several universally recognized considerations. For instance, research on children and youth always requires parental consent. Research on adults also requires informed consent and participants are never forced to participate. Confidentiality and anonymity are two additional practices that ensure the safety of participants when sensitive information is provided (e.g., sexuality, income, etc.). To ensure the safety of participants, most universities maintain an institutional review board (IRB) that reviews studies that include human participants and ensures ethical rigor.
It has not always been the case that scientists interested in studying humans have followed ethical principles in their research. Several studies that, when brought to light, led to the introduction of ethical principles guiding human subjects research and Institutional Review Boards to ensure compliance with those principles, are worth noting, including the Tuskegee syphilis experiment, in which 399 impoverished black men with syphilis were left untreated to track the progress of the disease and Nazi experimentation on humans. A recent paper by Susan M. Reverby  found that such unethical experiments were more widespread than just the widely known Tuskegee study and that the US Government funded a study in which thousands of Guatemalan prisoners were infected with syphilis to determine whether they could be cured with penicillin. Ethical oversight in science is designed to prevent such egregious violations of human rights today.
Sociologists also have professional ethical principles they follow. Obviously honesty in research, analysis, and publication is important. Sociologists who manipulate their data are ostracized and can have their memberships in professional organizations revoked. Conflicts of interest are also frowned upon. A conflict of interest can occur when a sociologist is given funding to conduct research on an issue that relates to the source of the funds. For example, if Microsoft were to fund a sociologist to investigate whether users of Microsoft's product users are happier than users of open source software (e.g., Linux, LibreOffice), the sociologist would need to disclose the source of the funding as it presents a significant conflict of interest. Unfortunately, this does not always happen, as several high profile cases illustrate (e.g., the Regnerus Affair). But the disclosure of conflicts of interest is recommended by most professional organizations and many academic journals. A comprehensive explanation of sociological guidelines is provided on the website of the American Sociological Association.
What Can Sociology Tell Us?
Having discussed the sociological approach to understanding society, it is worth noting the limitations of sociology. Because of the subject of investigation (society), sociology runs into a number of problems that have significant implications for this field of inquiry:
- human behavior is complex, making prediction - especially at the individual level - difficult or even impossible
- the presence of researchers can affect the phenomenon being studied (Hawthorne Effect)
- society is constantly changing, making it difficult for sociologists to maintain current understandings; in fact, society might even change as a result of sociological investigation (for instance, sociologists testified in the Brown v. Board of Education decision to integrate schools)
- it is difficult for sociologists to strive for objectivity and handle the subjective components of scientific practice - especially when the phenomena they study is also part of their social life
While it is important to recognize the limitations of sociology, sociology's contributions to our understanding of society have been significant and continue to provide useful theories and tools for understanding humans as social beings.
Blackstone, Amy. Principles of Sociological Inquiry: Qualitative and Quantitative Methods.
Earl Babbie, The Practice of Social Research, 10th edition, Wadsworth, Thomson Learning Inc., ISBN 0-534-62029-9
Glenn Firebaugh, Seven Rules for Social Research, Princeton University Press, 2008, ISBN 978-0-691-13567-0
W. Lawrence Neuman, Social Research Methods: Qualitative and Quantitative Approaches, 6th edition, Allyn & Bacon, 2006, ISBN 0-205-45793-2
Kleinman, Sherryl. 2007. Feminist Fieldwork Analysis. Sage Publications, Inc.
Charmaz, Kathy. 2006. Constructing Grounded Theory: a Practical Guide through Qualitative Analysis. Los Angeles, CA: SAGE.
Bruce Berg. Qualitative Research Methods for the Social Sciences, 7th edition.
Blumer, Herbert. 1969. Symbolic Interactionism: Perspective and Method. Englewood Cliffs, NJ: Prentice Hall.
see also chapter on Sociological Practice additional reading
- Why is human behavior hard to study?
- Is sociology a science like the natural sciences (e.g., biology, physics, chemistry)? How?
- What are the limitations of sociological inquiry?
- How does sociological inquiry move beyond common sense?
- Kuhn, Thomas S. 2012. The Structure of Scientific Revolutions, 50th Anniversary, 4th Edition. Chicago, Illinois: University of Chicago Press.
- Moghissi, A. Alan. 2010. “Peer Review and Scientific Assessment.” Technology & Innovation 12:187-188.
- Weber, Max. 1946. Science As Vocation. Gerth, H. H. and Mills, C. Wright, Editors and Translators. From Max Weber: Essays in Sociology. New York: Oxford University Press; pp. 129-156.
- Reverby, Susan M. 2011. "Normal Exposure" and Inoculation Syphilis: A PHS "Tuskegee" Doctor in Guatemala, 1946-48. Journal of Policy History.