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st: RE: Interpreting interactions in logistic


From   "Kieran McCaul" <Kieran.McCaul@uwa.edu.au>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Interpreting interactions in logistic
Date   Mon, 7 Sep 2009 09:18:45 +0800

...

You have fitted an interaction between _Iactive_sexlife.
So, in the first model, the effect of _Iactive_sexlife is 1.828036.  This is the effect for males.  The effect for females is 1.828036*.7020524=1.2833771 which is what you get in the second model.



______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
Phone: (08) 9224-2701
Fax: (08) 9224 8009
email: Kieran.McCaul@uwa.edu.au
http://myprofile.cos.com/mccaul 
http://www.researcherid.com/rid/B-8751-2008
______________________________________________
If you live to be one hundred, you've got it made.
Very few people die past that age - George Burns


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Filipa de Castro
Sent: Monday, 7 September 2009 8:26 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Interpreting interactions in logistic

Dear statalisters,

I am writing to ask for some help in interpreting the result of a
model I am running with my data.
I have already asked some colleagues and the responses have been quite
contradictory.
So. My model is:

Y= suicidal ideation 0=no 1=yes
X1= male 0=female  1=male
X2= active_sexlife  0=no  1=yes

Y= X1  X2   X1*X2

the output for this is:

Survey: Logistic regression
Number of strata   =         3                     Number of obs
=     12424
Number of PSUs     =       217                  Population size    =    992834
                                                             Design df
         =       214
                                                              F(   3,
  212)    =    131.24
                                                             Prob > F
         =    0.0000
------------------------------------------------------------------------------
             |                              Linearized
ideacion_cat        | Odds Ratio   Std. Err.      t    P>|t|     [95%
Conf. Interval]
-------------+----------------------------------------------------------------
_Iactive_sexlife |   1.828036   .1347743     8.18   0.000     1.580781
   2.113964
       Male          |   .4996468   .0253318   -13.69   0.000
.4521289    .5521587
    _IactXmale    |   .7020524   .0743205    -3.34   0.001
.5698326    .8649516
------------------------------------------------------------------------------

Now. If I do the same model but instead of Male I use Female where
0=male and 1=female  I get this:

Survey: Logistic regression
Number of strata   =         3                  Number of obs      =     12424
Number of PSUs     =       217                  Population size    =    992834
                                                Design df          =       214
                                                F(   3,    212)    =    131.24
                                                Prob > F           =    0.0000
------------------------------------------------------------------------------
             |                   Linearized
ideacion_cat |     Odds Ratio   Std. Err.      t    P>|t|     [95%
Conf. Interval]
-------------+----------------------------------------------------------------
_Iactive_sexlife |   1.283377    .110368     2.90   0.004     1.083268
    1.52045
        female      |   2.001414   .1014703    13.69   0.000
1.811074    2.211759
    _IactXfemale |   1.424395    .150789     3.34   0.001     1.156134
   1.754901
------------------------------------------------------------------------------

My questions are:
Why does the model change just by inverting the dummy for sex?
How can I know the effect for _active_sexlife for man and woman from
just looking at one of the outputs?
If I look at model 1 I see that odds for having suicide ideation for
males is 49%, but when I look at model 2 I see that odds for women is
200% ? I am really puzzled with this result as well as with the
different OR for the vars in 2 models.
If the interaction is significant how come I cannot detect it
graphically with postgr3?? neither by a tabulation of sex x
active_sexlife  with prtab ?


--------------
best wishes and thanks
Filipa de Castro

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