Dear Clive et al,
Thanks so much for your excellent answer, I highly appreciate it. I
have a follow-up question. Suppose, we run seperate OLS and have one
significant coefficience; however the interation term in the signle
equation is not significant. Waht can we say about the result.
Many thanks!
Quang
On 9/26/07, Clive Nicholas <[email protected]> wrote:
> Quang Nguyen wrote:
>
> > Suppose, I had a simple model: Y = Ao + A1*X1 + A2*Z + u, where A2 is
> > a categorical variable e.g., ethnicity. If I wanted to see how
> > different ethnic groups may have different effect on Y, I can use an
> > interaction term: X1*Z or I can run seperate equations fro each
> > ethnicity. Can you tell me the advantage of each method?
>
> Jaccard (2001: 17) provided a very good reason to prefer the
> single-model analysis in the case of logistic regression. I don't
> believe it would be any different for OLS regression:
>
> "It is not uncommon for researchers [to calculate] seperate logistic
> regression equations for males and females and then examining whether
> the logistic coefficient for X is "statistically significant" (i.e.,
> has an associated p value less than 0.05) in both analyses. If the
> coefficient is statistically significant in one group but not in the
> other, then the conclusion is that X is more important for the one
> group than for the other. This logic is flawed because the researcher
> never performs a formal statistical test of the _difference_ between
> the logistic coefficients for the two groups."
>
> You will, of course, note that is there is now a substantial research
> literature that has called into question the practice of using
> interaction terms in LR models (much of which has been discussed on
> this list), but I hope this helps to answer the original question.
>
> --
> Clive Nicholas
>
> [Please DO NOT mail me personally here, but at
> <[email protected]>. Thanks!]
>
> Jaccard J (2001) "Interaction Effects In Logistic Regression", QASS
> Series 135, Thousand Oaks, CA: Sage.
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