[R] Effect size, interactions, and main effects (Stats Question) [ffmanova]

[Mark Difford]

Dear List Members,

I would very much appreciate any pointers you could give me on the following
matter:

Main Question:
To what extent does the "rule" that it is unreasonable to talk about main
effects if there are significant interactions in a model depend upon effect
size [of the significant interaction terms]?  Or is this not an issue?

More practically:  Suppose I were to carry out a so-called Type-II MANOVA
(using ffmanova) and were to find that the interaction term in a 2-way
analysis has borderline significance (say p = 0.045) and a small effect
size, whereas one of the main effects is highly signficant (say p = 6.8e-10)
and has a large effect size.

Would it in this case be reasonable for me to ignore the interaction term,
and talk only about main effects?  And, presuming the main question is fair,
are there general guidlines concerning the relationship between level of
significance and effect size for interaction terms.

Thank you in advance for your help,

Mark.


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