Ummm, yes there are. I always start with a saturated model, use Normal, Bayes and Pareto plots as well as look at parameter effects and SS. Use engineering knowledge and predictions about potential effects to then reduce the model. I really don't place much weight on p-values as YOU control these via how you collect your data. So, for example, if you leave the interaction out of the model, guess what...the interaction is the estimate of mean square error and is therefore the basis of the statistical test (F-test). For unreplcated designs, I much prefer the unbiased Daniels plots, but that's just me.
"All models are wrong, some are useful" G.E.P. Box