On Apr 15, 2013, at 14:30 , ilovestats wrote: > Hi, I'm trying to decide between doing a FA or PCA and would appreciate some > pointers. I've got a questionnaire with latent items which the participants > answered on a Likert scale, and all I want to do at this point is to explore > the data and extract a number of factors/components. Would FA or PCA be most > appropriate in this case? > Cheers, > Hannah > >
Not really an R question, is it? Stats.StackExchange.com is -----> that way! In terms of theory, PCA is essentially FA with the same residual variance in all responses. With all-Likert scales, it is unlikely that there will be much of a difference. In practical terms: - factanal can diverge (Heywood cases) which is a bit of a bother on the other hand - factor rotation is based on factanal() output; may require a little extra diddling to work with prcomp(). I think I'd try factanal() first, and if it acts up, switch to prcomp(). -- Peter Dalgaard, Professor Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
