This can happen because a step in the selection process consists of adding or dropping a single variable. Note that if a variable is added it is entirely possible that one or more of the other variables could have their p-values change to be larger than the SLS= criterion. Since the models at all of the steps are evaluated by the CHOOSE= criterion and one is selected as the final model, that model could be one where one or more of its variables has p-values greater than the SLS= criterion. If you want to ensure that this doesn't happen, you need to remove the CHOOSE= option so that the model in the last step is chosen.  ... View more