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DoE analysis : Removing non significant main factors when they are implicated in a significant interaction

Hey Jmp users,

 

Is it possible to remove non significant main factors when they are implicated in a significant interaction in DoE data analysis ?

 

Thanks !

 

I

DOERe: DoE analysis : Removing non significant main factors when they are implicated in a significant interaction

Hi,

 

Yes, it is possible. When analyzing a DOE using Fit Model, the lower-order effects that are contained within higher-order effects are denoted with a circumflex symbol (see image below).  You will get a warning message if you try to remove it, with the option to "remove only non-contained effects," but you have the option to ignore this.   

 

 

Re: DoE analysis : Removing non significant main factors when they are implicated in a significant interaction

Hi,

Thanks for your answer. I wanted also to ask from a statistical point of view, does it affect your model ?

 

Re: DoE analysis : Removing non significant main factors when they are implicated in a significant interaction

Hi,

 

The principle is called "effect heredity."  There is a brief mention of it in the "Principles and Guidelines for Experimental Design" (https://www.jmp.com/support/help/en/15.2/index.shtml#page/jmp/effect-heredity.shtml).  Basically, if a main effect isn't significant than the higher-order term likely isn't significant either.

Re: DoE analysis : Removing non significant main factors when they are implicated in a significant interaction

Actually the principle is Effect HIERARCHY.  That is if higher order terms are significant, then the lower order terms should be included in the model.  (Heredity principle is the for a higher order term to be active at least one of the lower order terms (parents) must be active).

 

To be clear, what you really need to do is understand the interaction.  Since an interaction is when the effect of a factor depends on another factor, you need to understand this effect.  For 2-level designs, there are 4 possible factor combinations for 2nd order effects: -1,-1, -1,1, 1,-1, 1,1.  A significant interaction means there is a difference in effect between when the factors are at the same level vs. when they are at opposite levels.