I don’t fully understand what controlling for a factor in a experiment means, especially when it comes to real world studies with large number of people in the trials. For e.g. ” Yogurt consumers had a higher DGAI score (ie, better diet quality) than nonconsumers. *Adjusted for demographic and lifestyle factors and DGAI*, yogurt consumers, compared with nonconsumers”
Looking for an intuitive way to understand what controlling for factors means.
Thank you in advance!
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There’s a lot of ways to do it, but ultimately it means trying to do what you can to make it so those differences don’t matter.
One of the cheapest/easiest ways to do that is to make it so both groups have a roughly equal makeup of people for the factors you are trying to control. So you mention:
>Adjusted for demographic and lifestyle factors
This likely means that both groups the yogurt consumers and the yogurt nonconsumers had a similar makeup. The same percentage of low-income earners, high-income earners, old people, young people, etc. And for lifestyle, it’s probably talking about how often people work out or stuff like that. So you got the same mix of sedentary people and active people in each group.
So in this case you want basically the only difference between each group to be whether they eat yogurt or not.
This means that any differences in the data are unlikely to be the result of the demographics or lifestyle because each group basically had the same demographics and lifestyle make up.
Obviously, this isn’t perfect and you rarely get the *exact* same makeup. But a good research team will do there best to get as close as possible to that.
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