|Date: ||Sun, 21 Jan 2007 22:22:51 +0100|
|Reply-To: ||"adel F." <adel_tangi@YAHOO.FR>|
|Sender: ||"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>|
|From: ||"adel F." <adel_tangi@YAHOO.FR>|
|Subject: ||RE : Re: contribution of independent variables in a random
intercept model (mixed NLmixed gllmmix )|
|Content-Type: ||text/plain; charset=iso-8859-1|
The example of students nested within schools, is the classical example of multilevel models.
Of course, radom intercept model is just an example.
My question was about contribution of each indepedent vaiable in the variance of the dependent variable in a multilevel model.
David L Cassell <davidlcassell@MSN.COM> a écrit :
> I have a continuous depedent variable y and 3 independent variable X1
>(binary), X2 continuous and X3 (categorical with 3 categories)
> I have students nested within schools
> I am considering a random intercept model
> y = a + X1+X2+X3+ u+ e
> u the school residual
> e the student residual
> My question how I can compute the contribution of each of X1,X2 and X3
>in the variation of y, using mixed, NLmixed or gllmmix
> Thanks a lot for your suggestions
This is going to sound like a post I jsut wrote a few seconds ago, but
isn't this a survey sample problem? You have a sample of schools, with
a sample of students pulled from each. That sounds like a survey sample
analysis problem to me.
This means that the random intercept model may be the wrong approach.
What are you trying to estimate, and why are you using a random
intercept model, and what do you want to achieve (in a larger sense)
with your model?
David L. Cassell
3115 NW Norwood Pl.
Corvallis OR 97330
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