# Exploratory Factor Analysis for Binary Logistic Regression Variable Selection

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!

• Definitely think about using a principal component analysis to reduce teh dimensionality of your problem. – user25658 Aug 27 '13 at 5:21
• @BabakP, I use PCA as well to reduce dimensionality; however, as I stated, I like applyinig different methods to see how they compare in terms of variable selection. Thanks for the suggestion though! – Matt Reichenbach Aug 27 '13 at 12:06

Also, you should explore the dimensionality of the data - bearing in mind the need to extract meaningful factors. Check out R functions like VSS().