In mathematics, the students study about two types of basic quantities - the variables and the constant. A constant does have fixed value which can not be changed. On the contrary, a variable refers to "able to vary".
DefinitionBack to Top
The purpose of a research or an experiment is to provide a situation in which the difference between the conditions is actually the difference in the independent variable. If there exists some other variable which is changing with the change in independent variable, then this variable will be confounding variable. The specific definitions of confounding variable may vary in words. But, this variable should necessarily fit the following four criteria.
Let us suppose that there is a variable of interest denoted by "X". The confounding variable is represented by "C" and the outcome of interest be denoted by "O", then :
1) C is directly or inversely associated with O.
2) C is also associated with O just independent of X.
3) C is inversely or directly associated with X.
4) C does not in the direct pathway from X to O; i.e C is actually not a direct consequence of X.
Extraneous And Confounding VariablesBack to Top
Extraneous variables add up an error to the research or experiment, therefore they are termed as undesirable variables. In a research, the main goal of researcher is to control or decrease the effect of the extraneous variables as much as possible. These are the variables that are not a direct part of the manipulation.
We can say that these are the factors that we have not controlled. Extraneous variables affect the result. But usually they do not create any bias in the result. More specifically, a variable that the researcher is not studying intentionally and is able to threaten the validity of the result is known as an extraneous variable.
Confounding and extraneous variables are closely related to each other. When an extraneous variable changes along with the studied variables systematically, it is known as a confounding variable.The researcher's goal is to try to control the extraneous variables such that they do not become confounding variables. Extraneous variables do not create bias but confounding variables produce bias in the experiment.
ExamplesBack to Top
1) Confounding variables are used in risk assessment factors; such as gender, age and educational status. These usually effect on the health status and therefore are supposed to be controlled.
2) Another example is about the study of smoking or tobacco use on human health. These habits are correlated with the risks of heart, lung and various other diseases.
3) Confounding variables are also seen in the review of occupational risk assessments; for example in safety in coal mining.
4) According to a concrete example, a study about the relation between order of birth ( such as - 1st child, 2nd child and so on) and the possibility of Down Syndrome. In which the age of the parents may be a confounding variable; since the higher the maternal age the more chances of Down Syndrome in the child. So, Down syndrome is directly related to the maternal age.
5) In an example of the study consumption of ice cream for a given period. The confounding variables may be climate condition such as temperature, children's vacations etc; which directed correlate to ice cream consumption.