A confounding variable is a third unmeasured variable that impacts both the cause and effect in a research study.

Considering the effect of confounding variables is essential to prove the validity of the results.

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The definition of confounding variable

Also known as confounding factors, confounding variables are a type of extraneous variable linked with both the dependent and independent variables of the study. For a variable to qualify as a confounding variable, it must meet two criteria. These include the following:

• There should be a correlation between the variable and the independent variable. It can either be a causal relationship or not.
• The dependent variable should be causally linked with the variable.

Confounding variable: Example

You have been asked to collect data on cases of sunburn and consumption of ice cream in a particular area. You find that those with a higher level of ice cream consumption have suffered more cases of sunburn. So does that mean that consumption of ice cream is causing sunburn?

In this case, the outdoor temperature is the confounding variable. As the temperature rises, so does the consumption of ice cream and more chances of getting sunburn.

What is the importance of confounding variables?

Confounding variables play a huge role in research studies as an internal validator. Failing to factor in the influence of confounding variables may yield different outcomes than anticipated, and the actual relationship between the variables remains unclear.

For example, you might be interested in proving a cause-effect relationship even if there is none. If the confounding variable influences the dependent variable and not the independent one, there would not be a way for you to deduce it conclusively.

You find that states with a higher minimum wage see a much higher number of employees. So, does that mean a higher minimum wage is the only cause of high employment?

It is not always the case. For example, higher employment in the state might result from better job markets and not due to higher wages. For your analysis, you will need to consider the past trends in employment to find the impact higher wages have on employment rates.

Even if the cause-effect has been properly identified, confounding variables might cause you to over or underestimate the independent variable’s impact on the dependent one.

You find that infants born to mothers, who smoked, weighed much less when compared to the infants of non-smoking mothers. But it is also essential to account for the unhealthy habits like drinking or consuming unhealthy food that smokers regularly engage in if you want to find the conclusive connection between smoking and low body weight after birth.

Reducing the impact of confounding variables

Several methods can be used to limit the impact of the confounding variables on the outcome of your research. These methods can be used for any subject type, including humans, animals, plants etc. However, each of them comes with its own set of pros and cons.

Restriction: The method requires the restriction of the treatment group based on similar characteristics of the participants. It means that only the subjects with equal values of confounding factors are included in the treatment group.

Since the factors are similar across all the participants, they do not confound the cause-effect relationship and correlate with the independent variable.

Example of restriction Suppose you are looking to study if switching to a low carb diet can reduce weight. Since it is already established that weight loss can be influenced by several factors like age, exercise levels, etc., you restrict your control group to include only 30-year-old males who exercise regularly.

• ✓ Implementation is relatively simple.
• × The sample can be greatly restricted.
• × Other potential confounding factors might not be considered.

Matching: In this method, the sample group is selected according to similarities with the treatment/experiment group. In simple words, each participant of the sample group should have a corresponding counterpart with different independent variable values but the same potential confounders in the treatment group.

Example of matching
In your study, the subjects are matched based on confounding factors like sex, age, education levels, etc. It allows you to have a much wider control group that includes both male and female participants from several different backgrounds.

Each participant who is not on a low carb diet (control group) is matched with another participant who is on a low carb diet (treatment group) with similar characteristics like age, education level, etc. So, for every highly educated 25-year-old man on a low carb diet, you will need a highly educated 25-year-old man who is not on a low-carb diet and compare weight loss among both these subjects. Then, repeat the steps for all the other subject pairs.

• ✓ More inclusive than restriction.
• × Difficult to implement since subjects are needed to be paired according to every potential confounding variable.
• × Confounding variables can also include any variable that can’t be matched among the subjects.

Statistical control: If the data for the study has already been collected, possible confounders can be introduced as control variables when creating your regression models. It is an excellent way of limiting the effect of confounding variables on the outcome.

Regression model results will allow the effect of the confounding variable on the dependent variable to show up so you can easily isolate its effect on the independent variable.

Example of statistical control
After collecting weight loss data from different participants and data about what type of diet they follow, confounding factors like age, sex, workout levels should be introduced as control variables in your regression model. It will allow you to isolate the impact diet has on weight loss from the influence of the control variables.

• ✓ Can be easily implemented.
• ✓ Can be done after the data has been collected.
• × Directly observable confounders can be controlled, but unaccounted confounding variables will remain.

Randomisation: Randomising the value of the independent variable is a great way of reducing the impact of confounding variables on the outcome. For instance, participants can be drafted randomly into the control and treatment groups.

This ensures that the sample size is large enough and all the potential variables, even those not directly observable, will be averaged out across both groups. Furthermore, since the confounders are randomly placed in groups, they cannot affect the study’s outcome.

Since all potential confounders are accounted for in this method, randomisation is often considered the best method to limit the effect of confounding variables.

Example of randomisation
For your study of finding the relation between a low-carb diet and weight reduction, you find many subjects to take part in your study. The subjects were randomly assigned to the control and the treatment group.

This will ensure that both the groups will not only have the same average education, age and workout level but will also have the same average values of the confounders that are not measured or observed.

• ✓ All the possible confounders are accounted for, even the ones that are not observable directly.
• ✓ One of the best methods to limit the effect of confounding variables.
• × More difficult than any other method.
• × Needs to be done before data collection.
• × You need to ensure that only the treatment group get the treatment and not the control group.

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