Level setting should be representative and practical. Normalization (-1,+1) will take care of this to some extent. If the level setting for one factor is in a relative sense much bolder than the level setting for other factors, it will appear that factor is more significant. For screening designs, My advice has always been to error on the side of making sure all factors are set as bold as possible (This decreases the likelihood of imbalance of level setting across factors). One test of the boldness of level setting is if in your predicted response to a factor showing up as insignificant you suggest setting the levels farther apart in the next study….you should have set them farther apart originally. So predictions can help! This also depends on what you are trying to do with the DOE. If you are trying to explain a phenomena that is already occurring, then the representative level setting is a good idea. If you are in early design or looking for solutions, the representative is not necessary and the advice should be bold but reasonable.I would caution however that if the level setting is really bold, you may have trouble with non linearity, which could make the factor appear non significant when really it is. So the reasonable part of “bold but reasonable” is also important. That is where predictions are very useful – but tricky if prior knowledge is very low.