```Date: Wed, 15 Nov 2006 05:23:39 +0100 Reply-To: Hector Maletta Sender: "SPSSX(r) Discussion" From: Hector Maletta Subject: Re: PCA factor score uses? Comments: To: "Fredric E. Rose" In-Reply-To: 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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