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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.
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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.
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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.
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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.
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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. 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.
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 experiment appears successful - i. 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. 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.
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. 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. 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 .
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. 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.
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.
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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. 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 positive correlation means that as one variable increases e. A negative correlation is just the opposite; as one variable increases e.
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:. An example may help explain the difference.
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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.
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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 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.
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.