Huxley schrieb:
>
> 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?
>
> Huxley
Loadings of factor f1,f2 for items x1,x2,x3,x4...
f1 f2
x1 0.4 0.6
x2 0.3 0.9
x3 0.2 -0.1
x4 -0.8 -0.4
...
Call this loadingsmatrix A, your correlation-matrix R
That means, that A*A' = R
Call your empical datamatrix (x1,x2,x3,...) X
Call the unknow factorscores SC
Then it is assumed that
A*SC = X
Then you must find inv(A) to be able to find SC:
inv(A)*A*SC = inv(A) *X
SC = inv(A)*X
If the shape of A is not square and/or the rank is lower
then its dimension, then you have to find a workaround to
compute the general_inverse of A.
I don't find it so simple ;-)
Gottfried.
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