A confounding variable is an outside influence that changes the effect of a dependent and independent variable. This extraneous influence is used to influence the outcome of an experimental design. Simply, a confounding variable is an extra variable entered into the equation that was not accounted for. Confounding variables can ruin an experiment and produce useless results. They suggest that there are correlations when there really are not. In an experiment, the independent variable generally has an effect on the dependent variable.
For example, if you are researching whether a lack of exercise has an effect on weight gain, the lack of exercise is the independent variable and weight gain is the dependent variable.
A confounding variable would be any other influence that has an effect on weight gain. Amount of food consumption is a confounding variable, a placebo is a confounding variable, or weather could be a confounding variable. Each may change the effect of the experiment design.
In order to reduce confounding variables, make sure all the confounding variables are identified in the study. Make a list of everything thought of, one by one, and consider whether those listed items might influence the outcome of the study. Understanding the confounding variables will result in more accurate results.
Examples of Confounding Variable:
A mother’s education
Suppose a study is done to reveal whether bottle-feeding is related to an increase of diarrhoea in infants. It would appear logical that the bottle-fed infants are more prone to diarrhoea since water and bottles could easily get contaminated, or the milk could go bad. However, the facts are that bottle-fed infants are less likely to get diarrhoea than breast-fed infants. Bottle feeding actually protects against illness. The confounding variable would be the extent of the mother’s education on the matter. If you take the mother’s education into account, you would learn that better educated mothers are more likely to bottle-feed infants.
Another example is the correlation between murder rate and the sale of ice-cream. As the murder rate raises so does the sale of ice-cream. One suggestion for this could be that murderers cause people to buy ice-cream. This is highly unlikely. A second suggestion is that purchasing ice-cream causes people to commit murder, also highly unlikely. Then there is a third variable which includes a confounding variable. It is distinctly possible that the weather causes the correlation. While the weather is icy cold, fewer people are out interacting with others and less likely to purchase ice-cream. Conversely, when it is hot outside, there is more social interaction and more ice-cream being purchased.
In this example, the weather is the variable that confounds the relationship between ice-cream sales and murder.
Another example is the relationship between the force applied to a ball and the distance the ball travels. The natural prediction would be that the ball given the most force would travel furthest. However, if the confounding variable is a downward slanted piece of wood to help propel the ball, the results would be dramatically different. The slanted wood is the confounding variable that changes the outcome of the experiment.
To ensure the internal validity of your research, you must account for confounding variables. If you fail to do so, your results may not reflect the actual relationship between the variables that you are interested in.
For 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).
Example if it is found that more workers are employed in states with higher minimum wages. Does this mean that higher minimum wages lead to higher employment rates?
Not necessarily. Perhaps states with better job markets are more likely to raise their minimum wages, rather than the other way around. 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.
Even 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.
Example you find that babies born to mothers who smoked during their pregnancies weigh significantly less than those born to non-smoking mothers. However, if you do not account for the fact that smokers are more likely to engage in other unhealthy behaviours, such as drinking or eating less healthy foods, then you might overestimate the relationship between smoking and low birth weight.
A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results. They can suggest there is correlation when in fact there isn’t. They can even introduce bias. That’s why it’s important to know what one is, and how to avoid getting them into your experiment in the first place.
In an experiment, the independent variable typically has an effect on your dependent variable.
For example, if you are researching whether lack of exercise leads to weight gain, then lack of exercise is your independent variable and weight gain is your dependent variable.
Confounding variables are any other variable that also has an effect on your dependent variable. They are like extra independent variables that are having a hidden effect on your dependent variables.
Confounding variables can cause two major problems:
Let’s say you test 200 volunteers (100 men and 100 women). You find that lack of exercise leads to weight gain. One problem with your experiment is that is lacks any control variables.
For example, the use of placebos, or random assignment to groups. So you really can’t say for sure whether lack of exercise leads to weight gain.
One confounding variable is how much people eat. It’s also possible that men eat more than women; this could also make sex a confounding variable. Nothing was mentioned about starting weight, occupation or age either. A poor study design like this could lead to bias.
For example, if all of the women in the study were middle-aged, and all of the men were aged 16, age would have a direct effect on weight gain. That makes age a confounding variable.
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