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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.
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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.
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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.
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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.
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.
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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. 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 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.
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.
Finally, quantitative sociologists generally attempt to utilize mathematical realities e. 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. Finally, qualitative sociologists generally attempt to utilize natural realities e.
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.