confounding variable
By Julia Simkus, published Jan 24, 2022
A confounding variable is an unmeasured third variable that influences, or “confounds,” the relationship between an independent and a dependent variable by suggesting the presence of a spurious correlation.
Due to the presence of confounding variables in research, we should never assume that a correlation between two variables implies a causation.
When an extraneous variable has not been properly controlled and interferes with the dependent variable (i.e. results) it is called a confounding variable.
For example, if there is an association between an independent variable (IV) and a dependent variable (DV), but that association is due to the fact that the two variables are bo
th affected by a third variable (C), then the association between the IV and DV is extraneous.
Variable C would be considered the confounding variable in this example. We would say that the IV and DV are confounded by C whenever C causally influences both the IV and the DV. In order to accurately estimate the effect of the IV on the DV, the researcher must reduce the effects of C.
If you identify a causal relationship between the independent variable and the dependent variable, that relationship might not actually exist because it could be affected by the presence of a confounding variable.
Even if the cause and effect relationship does exist, the confounding variable still might overestimate or underestimate the impact of the independent variable on the dependent variable.
How to reduce the impact of confounding variables
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It is important to identify all possible confounding variables and consider the impact of them in your research design in order to ensure the internal validity of your results.
Here are some techniques to reduce the effects of these confounding variables:
1.Random allocation: randomization will help eliminate the impact of confounding variables. You can randomly assign half of your subjects to a treatment group and the other half to a control group.
This will ensure that confounders will have the same effect on both groups, so they cannot correlate with your independent variable.
2.Control variables: This involves restricting the treatment group to only include subjects with the same potential for confounding factors.
For example, you can restrict your subject pool by age, sex, demographic, level of education, or weight (etc.) to ensure that these variables are the same among all subjects and thus cannot confound the cause and effect relationship at hand.
3.Within-subjects design: In a within-subjects design, all participants take part in every condition.
4.Case-control studies: Case control studies assign confounders to both groups (the experimental group and the control group) equally.
Suppose we wanted to measure the effects of caloric intake (IV) on weight (DV). We would have to try to ensure that confounding variables did not affect the results. These variables could include:
∙Metabolic rate: If you have a faster metabolism, you tend to burn calories quicker.
∙Age: Age can have a different effect on weight gain as younger individuals tend to burn calories quicker than older individuals.
∙Physical Activity: Those who exercise or are more active will burn more calories and could weigh less, even if they consume more.
∙Height: Taller individuals tend to need to consume more calories in order to gain weight.
∙Sex: Men and women have different caloric needs to maintain a certain weight.
Frequently asked questions about confounding variables
1. What is the difference between an extraneous variable and a confounding variable?
A confounding variable is a type of extraneous variable. Confounding variables affect both the independent and dependent variables. They influence the dependent variable directly and either correlate with or causally affect the independent variable.
An extraneous variable is any variable that you are not investigating that can influence the dependent variable.
2. What is Confounding Bias?
Confounding bias is bias that is the result of having confounding variables in your study design. If the observed association overestimates the effect of the independent variable on the dependent variable, this is known as positive confounding bias.
If the observed association underestimates the effect of the independent variable on the dependent variable, this is known as negative confounding bias.

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