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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The motivation there is a good faith effort to try to understand what is “really going on” because things and systems can be really complicated and lots of variables can come into play in unexpected ways.
An easy example is jobs. Let’s say you are trying to understand if more or less people are employed in a city compared to two years ago. So you take some data and look at various statistics. However, you recognize that jobs change throughout the year. In November and December the number of jobs increases because stores hire more people to deal with the holiday shopping season. Likewise when summer break hits and the college kids leave town and go home the jobs decrease from the school and from the places near the school etc.
So if you are going to make a good faith effort to try to determine if there are more or less jobs in your city now compared to two years ago, you can’t ignore these facts. If you compare the current year July jobs with the December jobs two years ago then you are doing it wrong. You have to find ways to account for or “control for” the typical fluctuations in jobs throughout the year.
This is a relatively simple example. Things that are more complex may require a lot of variables to control for.
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