I'm doing a linear regression model to look at how socio-economic status impacts children's height. My dataset will be survey data for both males and females.
To me, it makes sense to control for sex, as boys and girls obviously have different growth patterns. However, I've been told that with regression models you should only control for confounding variables - those that are associated with both the independent and dependent variables and could therefore create a spurious relationship.
So, should I instead be controlling for confounders, then using multi-level modelling to look at the relationship on both a male and female level? Or would I just collect info about sex in the survey and filter the dataframe by sex to run models for each? Or should I approach it a different way? OR should I in fact be including sex as a control variable?
Any help would be very very much appreciated!