I know that if you want to reduce variable size you have to use for instance principal component analysis. In addition to this, this method could eliminate multicollinearity problem in regression analysis.
In my research I have a lot of variables (about 30) of categorical data, which I want to reduce, and then use these results to predict categorical outcome (i.e. through logistic regression).
The questions would be:
1. Is it possible to get scores from correspondence analysis (similar as in Principal Component analysis) and to use it in logistic regression?
2. Is Correspondence analysis could eliminate multicollinearity problem?
Thanks in advance
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