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RE: st: increasing variance when adding covariates (xtmelogit)


From   Garry Anderson <g.anderson@unimelb.edu.au>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: increasing variance when adding covariates (xtmelogit)
Date   Thu, 19 May 2011 17:36:35 +1000

Hello Rob and Maria,

This situation can occur and is explained on pages 217 and 229 of Snijders and Bosker (1999)

Snijders T and Bosker R (1999) Multilevel Analysis: An introduction to basic and advanced multilevel modeling. Sage Publications.

Kind regards, Garry
 

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Maria Fleischmann
Sent: Thursday, 19 May 2011 5:07 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: increasing variance when adding covariates (xtmelogit)

Hello Rob,

did you yet get any answer on your question? perhaps on another way than statalist?
I would also be very interested the explanation because I had the same problem in one of my analyses. Back then, my supervisor told me that this was a rather frequent problem when modelling a multilevel logistic model. He however could not provide any explanations for this.
So if you get any more insight, I would be happy if you'd let me know too.

Maria

On 18 May 2011 13:44, de Vries, Robert <r.de-vries08@imperial.ac.uk> wrote:
> Hello all,
>
> I am getting some strange results from some multilevel logistic 
> regression models (persons nested within geographical areas, random 
> intercepts) run with xtmlogit.
>
> I am using xtmelogit to fit models predicting a binary outcome at the 
> person level from some person-level and area level predictors. In the 
> empty model (with no predictors) I get a certain estimate for the 
> standard deviation of the constant. However, when adding person and 
> country-level predictors to the model, the estimate of this property 
> actually INCREASES quite substantially (by about 15-20%).
>
> As I take the standard deviation of the constant to be an indicator of 
> (the square root of) the variance at the area level. It seems very 
> strange that this would go up when adding predictors to the model. I 
> would be grateful if anyone could shed any light on this situation.
>
> Rob
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