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.
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
