On Tue, 29 Jan 2002 10:52:30 +0100, "Huxley" <[EMAIL PROTECTED]> wrote:
> > Uzytkownik "Gottfried Helms" <[EMAIL PROTECTED]> napisal w wiadomosci > [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > > It's not so simple. You have to do matrix-inversion for > > that. > > > Not simple? I heard that taking suitable factor loadings and every variable > mean I can obtain this space. e.g. (I do not know is it true) > Let mean for car1 and questions 10 (variables): > mean X1=1 > mean X2=2 > .................. > mean X10=10 > I have 2 factor score. > factor loadins (aij) I have, therefore for first factor score, co-odrinate > for car1 is > F1(for car1)=1*a(1,1)+2*a(2,1)+3*a(3,1)+...+10*a(10,1) > is it true? No, that is not true. Please believe them. Factor loadings are *correlations* and serve as descriptors. They were neither scaled nor computed as regression coefficients - which is what you are trying to use them as. Now, in clinical research, we don't usually bother to create the actual, real, true factor, for our practical purposes. For practical purposes, it is important to have some face-validity for what the factor means. And it is handy for replication, as well as for understanding, if we construct a factor as the summed score (or average score) of a set of the items. So I look at the high loadings. For a good set of items, it can be realistic and appropriate to 'assign' each item to the factor where its loading is greatest, thus using each item just once in the overall set of several derived factors. (For a set of items where many items were new and untested, it can be appropriate to discard some of items -- where the loadings were split, or were always small.) Each factor is scored as the average score for of a subset of items. -- 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/ =================================================================
