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1 $begingroup$ proportional odds logistic regression would possibly be a smart approach to this problem, but I don't know if It really is out there in SPSS. $endgroup$
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Third, I don't use SPSS so I can't help there, but I might be surprised if it did not offer some types of nonlinear regression. Some options are quantile regression, regression trees and robust regression.
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Second, reworking info for making in fit a model is, in my opinion, the incorrect strategy. You would like your design to suit your trouble, not another way spherical. From the outdated times, OLS regression was "the one game in town" as a result of slow computer systems, but which is not accurate.
I had been just curious to find out (from an individual with your knowledge) Should the OP wouldn't home get to the exact dilemma. But, I believe you already answered :) $endgroup$
addresses this likely dilemma. For your fuller clarification, see this superb answer from @Affine: Collinearity diagnostics problematic only in the event the interaction expression is incorporated. Share Cite
$begingroup$ I want "solid motives" for the two centering and standardization (they exist fairly often). Generally speaking, they've got far more to complete with the info established and the situation than with the data Examination method.