I have done a Principal Component Analysis (rotated factors, varimax, etc.) which gave me 6 factors (F1, F2, F3, F4, F5, and F6). From this PCA, I saved the factor scores for regression (In SPSS, I used during my Factor Analysis: Scores| Save as variables| Regression). From what I understand, the regression factor scores in SPSS are standardized, with a mean = 0 and Std deviation = 1 (correct me if I’m wrong). The output of the regression factors are as follow;
Descriptive Statistics
N Range Minimum Maximum Sum Mean Std. Deviation
REGR factor score 1 for analysis 1 3063 11.01315 -6.39267 4.62048 .00000 .0000000 1.00000000
REGR factor score 2 for analysis 1 3063 11.32547 -2.29925 9.02622 .00000 .0000000 1.00000000
REGR factor score 3 for analysis 1 3063 9.35187 -5.19720 4.15467 .00000 .0000000 1.00000000
REGR factor score 4 for analysis 1 3063 11.58979 -4.17394 7.41585 .00000 .0000000 1.00000000
REGR factor score 5 for analysis 1 3063 23.94514 -4.10727 19.83787 .00000 .0000000 1.00000000
REGR factor score 6 for analysis 1 3063 11.26989 -2.97190 8.29799 .00000 .0000000 1.00000000
Valid N (listwise) 3063
Is it normal to have a result like above? Are the composite factor scores produced by SPSS are standardized?
Then I used the 6 factors as my independent variables along with other SES variables to explain BMI in multiple regression. How do I check for the absence of multicollinearity of the 6 factors, since r = 0?
Are the composite factor scores produced by SPSS are standardized?
1) Yes, they are (always produced as) standardized - don't you see it?. 2) What do you mean saying "composite" here? If this is your word for "weighted sum" or "linear combination" then it is all right. $\endgroup$