to amplifiy a bit, the interpretability of regression tends to go down as the assumptions of normality and homogeneous variance are markedly different from reality. You can still go through the calcualtions but the interpretation of results gets tricky. Factor analysis is a sort of regression analysis and so suffers in the same way from break downs of assumptions.
Rich Ulrich wrote: > On 1 Mar 2002 04:51:42 -0800, [EMAIL PROTECTED] (Mobile Survey) > wrote: > > > What do i do if I need to run a factor analysis and have non-normal > > distribution for some of the items (indicators)? Does Principal > > component analysis require the normality assumption. > > There is no problem of non-normality, except that it *implies* > that decomposition *might* not give simple structures. > Complications are more likely when covariances are high. > > What did you read, that you are trying to respond to? > > > Can I use GLS to > > extract the factors and get over the problem of non-normality. Please > > do give references if you are replying. > > Thanks. > > -- > Rich Ulrich, [EMAIL PROTECTED] > http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
