Restriction exampleYou want to study whether a low-carb diet can cause weight loss. Since these values do not differ among the subjects of your study, they cannot correlate with your independent variable and thus cannot confound the cause-and-effect relationship you are studying. In this method, you restrict your treatment group by only including subjects with the same values of potential confounding factors. Each method has its own advantages and disadvantages. You can use the following methods when studying any type of subjects- humans, animals, plants, chemicals, etc. There are several methods of accounting for confounding variables. How to reduce the impact of confounding variables However, if you do not account for the fact that smokers are more likely to engage in other unhealthy behaviors, such as drinking or eating less healthy foods, then you might overestimate the relationship between smoking and low birth weight. ExampleYou find that babies born to mothers who smoked during their pregnancies weigh significantly less than those born to non-smoking mothers. You must consider the prior employment trends in your analysis of the impact of the minimum wage on employment, or you might find a causal relationship where none exists.Įven if you correctly identify a cause-and-effect relationship, confounding variables can result in over- or underestimating the impact of your independent variable on your dependent variable. Perhaps states with better job markets are more likely to raise their minimum wages, rather than the other way around. Does this mean that higher minimum wages lead to higher employment rates? ExampleYou find that more workers are employed in states with higher minimum wages. This can lead to omitted variable bias or placebo effects, among other biases. If you fail to do so, your results may not reflect the actual relationship between the variables that you are interested in, biasing your results.įor instance, you may find a cause-and-effect relationship that does not actually exist, because the effect you measure is caused by the confounding variable (and not by your independent variable). To ensure the internal validity of your research, you must account for confounding variables. Here, the confounding variable is temperature: high temperatures cause people to both eat more ice cream and spend more time outdoors under the sun, resulting in more sunburns. Does that mean ice cream consumption causes sunburn? You find that higher ice cream consumption is associated with a higher probability of sunburn. It must be causally related to the dependent variable.Įxample of a confounding variableYou collect data on sunburns and ice cream consumption.This may be a causal relationship, but it does not have to be. It must be correlated with the independent variable.A variable must meet two conditions to be a confounder: confounders or confounding factors) are a type of extraneous variable that are related to a study’s independent and dependent variables. Frequently asked questions about confounding variablesĬonfounding variables (a.k.a.How to reduce the impact of confounding variables.
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