Michael I. Lichter wrote:
> This is a pretty basic question, but I haven't been able to find any
> examples in the lit with this particular configuration ...
>
> Suppose you regress Y on A and B, and you expect an interaction between A
> and B.
>
> In the regression Y = A + B, the coefficient for B is not significant, but
> you have reason to think that it will be significant once you introduce the
> interaction term.
>
> However, in the regression Y = A + B + AB, the coefficient for B remains
> non-significant even though the coefficient for AB is significant. Yet,
> "test A B AB" is significant.
>
> Is it reasonable to treat this as a significant interaction?
>
> What if AB is not significant either but "test A B AB" is still significant?
Woe betide anyone who dismisses any of Nick's flags on this list (!),
but I'm firmly with John on this one. As far as 'experts' working in
the fields of political science and political methodology are
concerned, this debate is all over bar the shouting. Including
first-order terms in an interaction model is no longer a nice-looking
optional extra.
What's important to remember here is that you only include the
first-order terms because:
(1) their exclusion would assume that they equal 0: which is almost
never the case;
and
(2) it allows you to get as precise a parameter estimate as possible
on the interaction term.
Braumoller (2004) - a member of this list - and Brambor et al (2006)
make these points and explain in detail what they consider to be good
practice in this area, at least from a social science perspective.
--
Clive Nicholas
[Please DO NOT mail me personally here, but at
<[email protected]>. Please respond to contributions I make in
a list thread here. Thanks!]
Brambor T, Clark WC and Golder M (2006) "Understanding Interaction Models:
Improving Empirical Analyses", Political Analysis 14(1): 63-82.
Braumoeller BF (2004) "Hypothesis Testing and Multiplicative Interaction Terms,"
International Organization 58(4): 807-820.
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