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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.
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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.
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.
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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:. A scientific method depends upon a careful characterization of the subject of the investigation. 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.
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.
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 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. 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. 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. Promotion would be the dependent variable. Change in promotion is hypothesized to be dependent on gender.
Scientists use whatever they can — their own creativity, ideas from other fields, induction, deduction, systematic guessing, etc. 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. Once a prediction is made, a method is designed to test or critique it.
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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.
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.