Hi,

Yes, that is all exactly correct (in general when you have a categorical factor with only two levels/categories -like sex, or the factor A in your example- you can typically define it with a single variable and treat it in your models in the same way that you would treat a continuous variable, like in your example model and contrast definitions).  

Best

Alfonso

Originally posted by jtanne98:

All, 

Interaction contrast construction has been thoroughly discussed on this forum - but some advice seems conflicting and I would like to be explicitly clear. The two things  Iwould like to be clear on are the inclusion of the variables individually and the construction of the ContXCont variables. 

As an example, I am wanting to look at the effects of a categorical (binary) variables A, and two continuous variables B and C on FC in my experiment. I also have age and sex as variables.  I have constructed the additional variables (AxB and BxC). Example values below on how that is constructed.

• 
  
  
  
  • Age = [ 18 20 22 21 23 19]
  
  
  • Sex  = [ 0   1    0   1   1   0 ]
  
  
  • A     = [1    1    1   0   0   0]
  
  
  • B     = [0.5   0.8    0.5   0.2   0.2   0.4]
  
  
  • C     = [5      3       2      9      1     2 ]
  
  
  • AxB = [0.5   0.8    0.5  0    0       0   ]
  
  
  • BxC = [2.5   2.4    1   1.8   0.2   0.8]
  
  
  
  

For any single variable A or B: I would select All, Age, Sex, and the variable, then use the contrast [0 0 0 1] 

For a contrast for A and B:  I would select All, Age, Sex, A , B, and AxB, then use the contrast [0 0 0 0 0 1] - keeping the indiviudal variables in the model but adjusted out.

This would tell me if there is a significant interaction effect of A and B on FC, while adjusting for age and sex, correct? 

Thank you for any clarification and/or confirmation.