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# Re: st: Testing interaction terms

 From Richard Goldstein <[email protected]> To [email protected] Subject Re: st: Testing interaction terms Date Wed, 19 Jun 2013 09:31:17 -0400

```to continue David's example a little - if you do what he says (using the
"hascons" option on your linear regression), you can then use a post-hoc
test to test whether you really have an interaction or whether the
effects of var1 and var2 are additive

Rich

On 6/19/13 8:25 AM, David Hoaglin wrote:
> Verena,
>
> Because the data have no observations for var1==0 & var2==0, it is not
> possible to express the combined effect of those variables (in the
> linear predictor) in the usual way,
> (effect of var1) + (effect of var2) + (interaction).
> One alternative approach is to treat the combination of var1 and var2
> as a categorical variable with three categories: var1==0 & var2==1,
> var1==1 & var2==0, and var1==1 & var2==1.
>
> David Hoaglin
>
> On Wed, Jun 19, 2013 at 8:06 AM, Verena Dill <[email protected]> wrote:
>> I obtained the coefficients from a regression model and want to test whether
>> or not the coefficients are significantly different from each other.
>> The problem now is that the two variables are related to each other like
>> interactions and only partly overlap.
>>
>> var1: variable 1 (dummy)
>> var2: variable 2 (dummy)
>> interaction: interaction of variable 1 and variable 2
>>
>> tab var1 var2
>>
>> var1         |            var2
>>              |         0          1      |     Total
>> -----------+----------------------+----------
>>          0   |         0        122      |       122
>>          1   |       322        256      |       578
>> -----------+----------------------+----------
>>      Total   |       322        378      |       700
>>
>> Since no observations exist for var1==0 & var2==0 I can only include the
>> interaction and one of the variables (just to mention that: From a
>> theoretical sense it makes sense to do so): "probit var1 interaction"
>> Now I want to test if I can reject the hypothesis that
>> _b[var1]=_b[interaction]. If I use the standard command "test" it does not
>> account for the fact that these variables are related.
>>
>> Because of the nature of my variables I wanted to use the "contrast" command
>> but this only works if I'd use something like this before: "probit
>> var1##var2"  which is not solvable because of the above mentioned fact that
>> var1==0 & var2==0 does not exist in the data.
>>
>> Can anybody suggest another command that takes into account that the two
>> variables are interacted or has ideas on how to adjust the
>> "contrast"-command?
>>
>> Any help is greatly appreciated!
>>
>> Verena
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