Model Selection for Designed Experiments with BlocksI sometimes get questions about how to select the "best" model from experiments with blocks. We recommend that you model blocks as random effects, which means that you can't use Stepwise or Generalized Regression personalities for model selection because they only support fixed effects.
In this video I talk about:
- Why we block experiments
- Fixed versus random effects
- REML modelling
- Model selection strategies with random effects
Re: Model Selection for Designed Experiments with BlocksHI @mjz5448 ,
The recommendation is to keep the block effect in. This is because it will affect the statistical significance tests on your factor effects. There is error that is properly attributed to the block and you don't want this to "contaminate" the error that is estimated for the factor effects. Hope that makes sense. In most cases it will not make a big difference to your conclusions.
Phil
Re: Model Selection for Designed Experiments with BlocksThanks @Phil_Kay ,
So are you saying the reason for leaving a non-significant block effect in a model as opposed to a non-significant factor effect is due to the fact that you're more certain (or maybe 100% certain?) that you can attribute a certain amount of error to the block, where as with main effects or higher order terms it become more ambiguous due to confounding effects etc..?
Re: Model Selection for Designed Experiments with Blocks@mjz5448 - sort of. We know that there is a source of error due to changes between blocks. So we should include the block effect to capture this source of error, even if it is not found to be statistically significant. This will mean that we have the best estimate of error for significance testing of our factor effects. Another very good reason for including the block effect is because @Mark_Bailey says that you should. That is always a good enough reason for me! 
