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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Hopefully someone can speak to the details of HOW they do it, but I can at least answer the WHAT part of the question. A study is trying to look for how thing A relates to thing B, but there’s usually also things C-Z that relate to A and/or B. Controlling for those things is trying to take them out of the answer as much as possible to isolate just A and B as best they can.
For example, say I did a study to see if owning a dog made you healthier. I might find that people who own dogs take more walks. To see if it’s really the dog making them healthier, I also want to look at people who do those things and don’t have a dog. Correcting for those variables would be looking at whether people that take as many walks as dog owners but don’t have a dog. The more variables that you can make the same, the better the chance that it’s your studied variable tied to the studied effect.
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