What are confounding factors?
When studies are designed to look at cause-and-effect relationships, researchers have to consider confounding factors.
What are confounding factors?
Confounding factors are variables that can indirectly or directly influence a study's results. For researchers to accurately determine whether one thing has a direct impact on another, they have to know about all potential factors that could affect the outcome.
Not only do they have to be aware of these extra factors, but they also have to design a study that controls for them to get the closest possible answer for the cause-and-effect they're exploring.
Examples of confounding factors
If I were to design a study to test whether vitamin D from the sun improved mood, it would not be as simple as asking people to be in the sun and then answer questions about their level of happiness. I would have to consider confounding factors like the potential positive effects of fresh outdoor air and sunshine. These exposures could improve the mood, while vitamin D might have no effect. If I don't consider the sun and fresh air as possible confounding factors, my study could falsely show that vitamin D = happiness, when in reality, it could be that sunshine = happiness, or fresh air + sunshine = happiness, or maybe all three together lead to happiness, but one factor on its own does not.
Similarly, what if I wanted to design a study to test whether coffee drinkers have cleaner houses? I can't just ask people who drink coffee if their house is clean. First, "clean house" can be defined in hugely different ways. Second, I have to know a lot about their routine and other aspects of their daily life to make sure that coffee is linked with the outcome of a clean house, and not something I didn't ask about. Perhaps the coffee produces more energy, which leads coffee drinkers to exercise more, which affects the person's mood or motivation to clean?
But what if my study found that coffee drinkers did not have cleaner houses? Could I say that drinking coffee = a messier house? No, because I have to consider the confounding factors. What if coffee consumption makes it harder to sleep at night, which impacts mood and motivation, which decreases the coffee drinker's desire to clean or alters their perception about how clean their house is?
If I really want to answer the question about coffee and a clean house, I have to think about all of the other things that could potentially interfere with the answer I want, and I have to find ways to address them.
This is why human research is complicated. It's never as simple as A = B. It's more like A + (B × C × D × E) - (F + G + H - I) ÷ (J + K + L) = M.
Why are confounding factors important?
Confounding factors can lead to false conclusions about a study's outcome. Reading the full text of a study, not just the abstract, is a critical way to see how a study was designed, including limitations and confounding factors.
Confounding factors can also influence the way we perceive information in the real world, too. Critically thinking about an issue outside of basic cause-and-effect results can help with decision-making and observations in daily life.