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RE: st: increasing variance when adding covariates (xtmelogit)
From
Garry Anderson <[email protected]>
To
<[email protected]>
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: [email protected] [mailto:[email protected]] On Behalf Of Maria Fleischmann
Sent: Thursday, 19 May 2011 5:07 PM
To: [email protected]
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 <[email protected]> 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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