Date: Wed, 15 Nov 2006 05:23:39 +0100
Reply-To: Hector Maletta <hmaletta@fibertel.com.ar>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Hector Maletta <hmaletta@fibertel.com.ar>
Subject: Re: PCA factor score uses?
In-Reply-To: <C17FC6F8.1857%frose@palomar.edu>
Content-Type: text/plain; charset="us-ascii"
Factor scores are standardized variables, with mean=zero and SD=1,
so it is normal that one group is below 0 and the other is above. One group
is high in whatever the factor represents (racist attitudes?), the other is
low.
If factors are orthogonal, i.e. independent of each other, they
represent different, uncorrelated underlying traits your observables
variables were measuring. If rotated obliquely they may show certain
correlation among themselves. You may treat the scores as dependent
variables. You may also interpret them according to the particular variables
associated with each factor (i.e. having high loadings on each factor).
Hector
-----Mensaje original-----
De: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] En nombre de
Fredric E. Rose
Enviado el: 15 November 2006 04:18
Para: SPSSX-L@LISTSERV.UGA.EDU
Asunto: PCA factor score uses?
I'm not entirely familiar with PCA and could use some help.
I've used PCA w/varimax rotation to reduce 10 variables (answers to
a racism
attitudes questionnaire) down to 2 factors. I want to know if the
calculated factor scores for each participant can then be used as a
dependent variable in subsequent analyses, or whether I should
simply
combine the variables loading on the respective factors and use
those? My
problem is in interpreting the factor scores: Group 1 has a mean of
-2.72
and Group 2 has a mean of 2.68. These are significantly different,
but I'm
not sure what the means represent (the raw data are scores ranging
from 1 to
10, so there are no negatives).
Thanks for any insight.
Fred Rose
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