I am new to this thread and was hoping someone could help with the following problem. I have a multivariate dataset where each of the 100 variables in measured in the same unit. My intention is to run a PROC princomp/factor technique which creates 100 independent variables and then I believe can then run univariate ANOVAs of each of these factors to find out what other variables in my dataset best discriminate this information. If I run a PROC FACTOR I can see what how much each of the total variation the factors explain. However, when I try to use PROC SCORES to create factor scores each of the factor scores are standardised to variance 1. This means in the multiple univariate ANOVAs I am doing, each factor score has the same weight even though they account for different proportions of variance explained. It appears to me if I multiply the factor scores by the square route of the Eigenvalue it would give me what I require - independent variables whose value reflects the variation they explain. Is this correct ? Alternatively I can run PRINCOMP and I think this would give the same answer above after the multiplication. The thing that strikes me about the above is that I have to write code to do this in the factor analysis. Is there a simple way to get non-standardized scores ? Perhaps because it is not there as an option, it is inappropriate to do this. Also, the PRINCOMP does not do rotations. In the example the dataset is only 100 variables but what if it was much more than this and I wanted to extract only a few variables but for those to maximise the variance. PROC FACTOR can do this but PRINCOMP does not have the varimax option. Do I have no choice but to do it in proc factor and adjust the scores as I have detailed ?
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