I have obtained optimally scaled variables from a highly mixed nature of data containing binary, nominal, ordinal and scale type variables. The optimal scaling was obtained in SPSS through a CATPCA procedure. Now I want to use these variables in a Factor Analysis and want to use a rotation to obtain meaningful loads. What I think is that I should avoid any normality assumption for these optimally scaled variables. So, maximum likelihood method of factor extraction is probably not applicable here (although I am not so good at FA, so not exactly sure). Should I use principal components method of factor extraction instead?
What else method can be useful that avoids distributional assumption?
If I want the factor scores in a further regression as IVs, will it be at all a good idea to use oblique rotation? I think I should try to keep the factors as uncorrelated as possible if I want them as IVs in a further regression. Is this concept right?
Thanks for any kind of suggestion. :)