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Dear statistical question team,(my last post got deleted, I assume that happened because I accidently posted it in the wrong topic, so I hope this is the right one)I am a german student from the RFH-Cologne. I am writing my bachelor thesis about preference structures and I am using the GPA to compare these sctructures (created with multidimensional scaling) with each other. When I use the GPA to compare these structures, I get a p-value for each dimension. As I understand, the smaller the p-value is, the more these structures are fitting (Which preference structures are the best match?). But because I am using the MDS, I can increase the numbers of dimensions, so the average value of p is getting smaller (Add all p-values of all dimensions and divide it by the numbers of dimensions). My questions are: 1. Is that thinking correct? 2. If so, does the GPA transform, rotate, etc. all dimensions together or each alone?Best wishesCarsten Münch from Germany
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