Hi, Thanks for answering my question. I will be doing a rotation (varimax) so I think staying with proc factor instead of proc princomp. Also, I need to create factor scores after i get the results of the PCA. Can I get factor scores if I use the cov matrix? As the for the link to the document, thank you for showing that. So one reason to pre specify type=corr is if you have a lot of observations and will be running factor analysis a lot. Otherwise, not specifying type, it just means it's taking more time to run but it's still using a correlation matrix? I'm asking this becuase i'm using the "A step by step approach to using SAS for factor analysis and structal equation modeling" By Hatcher and it says "data may be input in the form of raw data, a correlation matrix or a covariance matrix" and it shows examples of how to run on raw data (not extra data step is required) and then on a correlation matrix (where you need to specify data d1 type=corr and then input the correlation matrix. Is the only difference here what info you start off with? E.g. if I input raw data, there is no extra data step, but If I start with input data that's not raw (that's a correlation matrix) this would need to be specified first as an extra step? So really what i'm saying is that if i'm not worried about the time it takes to run, and i have raw data to input, there is no need to specify type=corr, and the underlying assumption is that i'm relying on a correlation matrix? If the data is put in 'raw form' as hatcher says, does this mean it's accounting for the different in variance on each likert scale question? e.g. if I believe it's important to take into account that some questions on the likert scale go to 6 and another goes to 12. E.g. I believe the question that goes to 12 should have more weight than the question that goes to 6. Thanks again for your help.
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