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# Re: st: Interpretation of interaction term in nonlinear models

 From Suryadipta Roy To statalist@hsphsun2.harvard.edu Subject Re: st: Interpretation of interaction term in nonlinear models Date Thu, 30 May 2013 10:52:45 -0400

```Dear Maarten and fellow Statalisters,

I actually had a related question as to whether there might be a
similar (one-sentence) interpretation in case of a three-way
interaction between the same categorical variable, the continuous
variable with another categorical variable (0/1). Of course, I can and
have used -margins - with -marginsplot- to show how the interaction
effects differ in the presence of this categorical variable, but I was
wondering if I could get some help with an easier interpretation. The
coefficient of the three-way interaction term (standardized continuous
by categorical by categorical) in the fixed effects Poisson regression
with the -irr- option is 0.74 and in the probability metric form is
-0.21. Once again, thank you very much for the help!

Sincerely,

On Thu, May 30, 2013 at 7:53 AM, Suryadipta Roy <sroy2138@gmail.com> wrote:
> Maarten,
> This is very helpful, thank you very much! For some reason, I thought
> that the z_phd variable in your example is a categorical variable as
> to standardize my continuous variable for a similar interpretation.
>
> Sincerely,
>
> On Thu, May 30, 2013 at 7:19 AM, Suryadipta Roy <sroy2138@gmail.com> wrote:
>> Dear Statalisters,
>>
>> I am studying the effect of an interaction between a categorical
>> variable (0/1) and a continuous variable (0-6) on the dependent
>> variable in a nonlinear model (using -xtpoisson-). The value of the
>> coefficient using the -irr- option is 0.90, while the size of the
>> interaction term in the probability metric form is, of course, -0.11
>> (exp(-0.11) = 0.90). My basic question is, if it might be possible to
>> have a one-sentence interpretation of the value of the coefficient in
>> the multiplicative form (0.90), e.g. something in the lines of "the
>> effect of the categorical variable increases by a factor of 0.9 (i.e.
>> a 10% reduction of the dependent variable) due to an 1% increase in
>> the continuous variable"? Any suggestion in this regard will be highly
>> appreciated.
>>
>> Sincerely,
>> *
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```