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In statistics, a variable is any attribute that we can measure about a population, used in a study. It is very important to carefully define the variables to be measured when creating a study.
Think of things that we could find out about people:
For a political poll, for example, you wouldn't necessarily need to know if a candidate was a smoker or the number pets they have. However, you might want to know about their age, gender, state, political affiliations, zip code, ethnicity, and city.
Since those variables could potentially have some bearing on a political poll. They are the variables of interest for this study--literally, the variables you would be interested in measuring.
However, if you were conducting a weight loss study, the political affiliation will likely not be a variable to measure, but favorite food might seem important.
Some studies try to determine a cause-and-effect relationship between two variables in that one variable causes the other. An increase in one corresponds to an increase or decrease in the other.
In those cases, we define the one that causes the other as the explanatory variable. In a study, you can have more than explanatory variable.
Then, variables that are the result are called response variables.
Examples of Explanatory and Response Variables | |
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Explanatory: Number of hours you study Response: Grade on the exam |
You might hypothesize that as you increase the number of hours that you study, your grade on the exam will increase as well. So the number of hours you study, therefore, helps to explain your grade. |
Explanatory: Average monthly temperature Response: Ice cream sales |
You might assume that as the temperatures get warmer, that ice cream sales would go up in kind. |
The word confounding refers to when two variables get mixed up with one another and you can't tell the effect of one variable from the effect of the other variable. The confounding variable is the one not accounted for in a study. It is an unseen variable that has a significant effect on the response variable and is also related to the explanatory variable.
IN CONTEXT
Suppose that a researcher wants to know whether a high protein diet will help lab rats gain more weight than a low protein diet. The researcher has 26 lab rats and she selects 13 of the smallest rats to receive the low protein diet and 13 of the largest to receive the high protein diet. At the end of the study, she weighs the rats to determine their weight gain and finds that the rats on the high protein diet gained more weight.
Can you think of anything that she did wrong in this study?
The answer involves the occurrence of confounding. Remember, confounding is when two variables get mixed up and you can't tell the effect of one variable from the effect of the other variable.
So in this case, the effect of the diets--whether or not the high protein diet caused the rats to gain more weight--was confounded by the fact that the heaviest rats were put on the high protein diet. It’s not clear if the high protein diets were effective at weight gain. Something else may have caused the weight gain since they were heavy already.
Therefore, these are the two variables of interest in the study. The high protein diet was supposed to be the explanatory variable. The weight gain was supposed to be the response variable. The researcher was going to try to figure out a link between the two.
However, because of the way she assigned the rats, only a limited conclusion could be drawn. She wasn't able to draw the direct conclusion that she was hoping for--and that is confounding. Confounding should be limited in experiments when possible.
Source: THIS TUTORIAL WAS AUTHORED BY JONATHAN OSTERS FOR SOPHIA LEARNING. PLEASE SEE OUR TERMS OF USE.