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Re: st: increasing variance when adding covariates (xtmelogit)
From
Maria Fleischmann <[email protected]>
To
[email protected]
Subject
Re: st: increasing variance when adding covariates (xtmelogit)
Date
Thu, 19 May 2011 09:55:03 +0200
Thanks, Garry!
On 19 May 2011 09:36, Garry Anderson <[email protected]> wrote:
> 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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>
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