I would like to carry out an exploratory factor analysis on multiply imputed datasets according to the methodology by Nassiri et al.
They have created an R package for this (mifa), but unfortunately, there doesn't seem an option to dictate which variables should be used as predictors for multiple imputations and which should be used for the EFA (they are not the same in my case).
I would still like to apply their methodology manually. They first estimate the covariance matrix from the imputed sets of data using Rubin's rules, and then apply the EFA on this combined covariance matrix.
My question is: is it ok to use the correlation matrix instead of the covariance matrix? Much of the EFA reading I've come across factor analyses the correlation matrix and it's what I've used for my complete-case analysis. I wonder if there are any cases when the covariances matrix should be used, and why?