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# st: interpretation for negative and positive slope combination of interaction term

 From Nahla Betelmal To statalist@hsphsun2.harvard.edu Subject st: interpretation for negative and positive slope combination of interaction term Date Fri, 10 May 2013 10:20:42 +0100

```Hi David, thank you for your reply, but can you kindly explain more or
give me a link or reference how having a log function can solve the
issue and how to do it plz. Do you mean taking the log of the
dependent variable only or both the dependent and all continuous
independent variables? And how can this help plz

Nahla

From: "David Crow" <david.crow@cide.edu>
To: <statalist@hsphsun2.harvard.edu>
Subject: st: interpretation for negative and positive slope
combination of interaction term
Date: Thu, May 9, 2013 11:34 pm

Yes, Nahla and David Hoaglin were absolutely right.  Many apologies.

David

On Thu, May 9, 2013 at 5:06 PM, David Hoaglin <dchoaglin@gmail.com> wrote:
> Dear Nahla,
>
> You are correct about the slope and intercept, as shown in Figure 7.8
> of that book.  In David Crow's simplified model, B2 does not
> contribute to the slope for MV.
>
> A major part of your difficulty comes from trying to make ratio
> interpretations of coefficients in a model whose coefficients can tell
> you about differences.  One usual way to get coefficients that have a
> straightforward interpretation as ratios is to analyze the dependent
> variable in a logarithmic scale.  If it is not appropriate to work
> with your dependent variable in a log scale, you may be able to use a
> generalized linear model with a log link function.
>
> David Hoaglin
>
> On Thu, May 9, 2013 at 3:51 PM, Nahla Betelmal <nahlaib@gmail.com> wrote:
>> Thanks for the reply David, but I think there is something not quite
>> right. If you check this file, p.133 figure 7.8
>>
>> http://www.sagepub.com/upm-data/21120_Chapter_7.pdf
>>
>> You will notice that in order to get the slope for the group with
>> Dummy= 1 (overconfident manager in my case), we should add the
>> coefficient of beta and gama ( MV and OC*MV in my case) , and to get
>> the intercept for those managers we should add the alpha and y ( my
>> model intercept and the coefficient of OC )
>>
>>  I found the same in other files, the problem is that all the examples
>> they provide both beta and gama are positive which makes the addition
>> and interpretation process easy.
>>
>>  my question is how to add and interpret when one is positive and the
>> other is negative . As the effect of MV on realistic managers is -
>> 0.0566  and on  overconfident managers is + 0.0596 , it moves from
>> negative to positive 0.003 (again the interaction is significant
>> although at 10 level). how many times MV effect overconfident managers
>> more than other managers ?
>>
>> I would really appreciate help in that
>>
>> Many thanks
>>
>> Nahla
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