![]() 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: hot 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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