Confounding Variable: Definition & Example

Instructor: Tracy Payne, Ph.D.

Tracy earned her doctorate from Vanderbilt University and has taught mathematics from preschool through graduate level statistics.

Confounded again! This lesson is about variables that send a confusing message. Learn about confounding variables, what they are, and how to prepare for them in the design of your study.

Confounding Variables are Extraneous Variables

When we conduct experiments, our goal is to demonstrate cause and effect relationships between the independent and dependent variables. Extraneous variables are neither of the variables under investigation, but rather they are additional variables that influence those variables under investigation. One type of extraneous variable is a confounding variable.

In cause and effect studies where only two variables are included in the design, there are no confounding variables. Confounding variables become a nuisance in studies with three or more variables exist.

Confounding variables are two or more independent variables whose effects are difficult to parse; the effect of one independent variable on the dependent variable cannot be distinguished from the other independent variable.

A Hypothetical Example of Confounding Variables

A very prestigious university wants to better understand the characteristics that lead to higher scores on the SAT mathematics test. One analyst theorizes the more math classes a student takes in high school will have the strongest effect on a student's math score while another analyst theorizes that the more experience the math teacher has will have the strongest effect on a student's math score.

Each analyst runs his and her own analysis using the same data, with only this difference: the first analyst uses highest math class as his independent variable, but includes teacher's experience in the design while the other uses teacher's experience as her independent variable, but includes highest math class in the design. Lo and behold, they find the same results!

Each of the analysts can make the claim that his or her independent variable explains observed differences on a student's SAT math score. How is this? The analysts run a third analysis and find that their independent variables have a very strong positive correlation. As the variable highest math class increases, the variable teacher's experience increases simultaneously.

These variables are confounded. A student whose highest math class is statistics is likely in the same class with the teacher who has the most experience. A student whose highest math class is pre-algebra is in the same class with the teacher who has the least experience. So, which variable do we use to explain why some students score higher on the SAT math test?

What To Do About Confounds?

There are statistical procedures that allow analysts to deal with confounding variables. The first step is to determine how correlated the confounded variables are. If they are too highly correlated, then it might be necessary to eliminate one of them from the analysis.

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