The stopping rules based on p-values were the first implementation of the Stepwise platform. The default values to enter and to leave are good in the absence of any prior knowledge about the data or the best model. (But then, if you knew the answer, you wouldn't need stepwise regression to search for the best fit.)
Stepwise does not guarantee that you won't under-fit or over-fit your data. It is a 'productivity' tool that saves you time examining inferior fits and gets you close to, if not at, the best fit.
Modern criterion include AICc and BIC. Both criteria attempt to minimize bias (under-fitting) while also minimizing variance.(over-fitting). They do not require absolute thresholds like the p-value stopping rules. I recommend that you use one of these stopping rules.
The history allows you to examine some key information of the different models encountered during the search path. You can select any of them using the radio buttons on the right side of this list. You can then return to the top and click Make Model (for the Fit Model dialog) or Run Model (for the Fit Least Squares platform) where you can make fine adjusts within the Effect Summary table.
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