I have a dataframe of individual observations, that I partitioned to create a training (0.7 prop) and a test set (0.3 prop).
I started by running an exploratory factor analysis (EFA) on the training set (with the
psych::fa function). Then, based on results, I fitted a CFA on the test set (with
lavaan), associating each observed variable with one latent factor based on its maximum loading.
I would like, then, to use these latent variables as new variables for other analyses (using factor analysis as a feature reduction method). I know it's best to do structural equation modelling, as those latent variables include error, but that's not the option I want to pursue in respect to the models I want to fit.
Anyway, should I use the results of the EFA or the CFA to predict the new factors on my initial dataframe?