I have a great interest in learning new methods(at least to me) of variable selection in regards to binary logistic regression when I am working with over 500 potential predictor variables and have the duty of selecting 8 to 15 variables to build a parsimonious predictive model without using the notorious stepwise techniques.
With that being said, I was wondering if anyone has any experience using
proc factor for binary logistic regression variable selection? I assume my factors will correlate, and thus I will use
promax rotation, but with the results of the Exploratory Factor Analysis (EFA), I will simply retain the variable within each factor that has the highest loading on its own factor (latent variables models would confuse the hell out of the end-user of 99.999% of my models!) for further variable reduction through another technique such as
randomForest until the number of variables is small enough to build a model that has fewer than 15 variables in it.
Does anyone have any thoughts in regards to this process? Any suggested readings or input would be greatly appreciated. Thanks!