...
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: [email protected]
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: [email protected] [mailto:[email protected]] On Behalf Of Filipa de Castro
Sent: Monday, 7 September 2009 8:26 AM
To: [email protected]
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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