I'm running an EFA using orthogonal/varimax rotation, and assigning variables to a factors based on maximum load (so only each variable only gets one factor). I then want to validate the model using SEM... since the rotation I used to determine the variable<->factor loads was orthogonal, is it "wrong" to let the factors in my model have a covariance with one another? (eg, using RAM: Factor1<->Factor2,theta,NA)
I ask, as I get a much better model fit if I allow for this to occur.
More explicitly, what does it actual mean for underlying factors to have a correlation between them?