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2 similar approaches to Tom's would be to use a U-statistic formulation for the estimating equation rather than a straight "independent variates" formulation as GEE does (the caveat being that you'll have to mathematically derive it, but it's just a long exercise) or alternatively, fit a mixed effects model using crossed effects for the variation (this is not a traditional crossed effects model, since the crossing is between nodes and not across observers).

best,
-tony

(on leave for at least a year at Novartis Pharma AG, Basel Switzerland).

On Tue, 30 Nov 2004, Tom Valente wrote:

> *****  To join INSNA, visit http://www.insna.org  *****
>
> Marshall:
> I've used the Huber-White/Sandwhich estimator and also used GEE
> (Generalized Estimating Equations).:
> Valente, T. W., & Vlahov, D.  (2001).  Selective risk taking among
> needle exchange participants in Baltimore: Implications for supplemental
> interventions.  American Journal of Public Health, 91, 406-411.
>
> - Tom
>
>
> Marshall W. Van Alstyne wrote:
>
>> *****  To join INSNA, visit http://www.insna.org  *****
>>
>> Greetings SOCNET, I have a question related to the issue of correlation
>> among multiplex relations asked a few weeks ago.
>>
>> Since social network measures count agent links, the metrics for
>> connected
>> agents are not independent samples.  In regression analysis, this
>> leads to
>> correlated error terms.
>>
>> Does anyone know a good fix for this that will allow you to draw
>> inferences
>> about regression coefficients? Can anyone pls suggest good articles?
>>
>> I can think of two methods.
>>
>> Proposed solutions:
>>
>>        a) Inflate the standard errors by a factor proportional to the
>> degree or
>> correlation among the social network variables.  This would widen the
>> confidence intervals on any regression coefficients.
>>
>>        b) We happen to have a very large email dataset.  So, perhaps
>> we can draw
>> distinct random samples of email messages from the database for each
>> agent
>> in order that the error terms would not be correlated.  The sample sizes
>> would seem to depend on the social network sizes and also replacement.
>> Given that we have about six dozen people and 45,000 email messages, we
>> have a lot of room to draw large quasi-independent random samples.
>>
>> Any thoughts or recommendations would be really welcome.  Thanks.
>>
>> MVA
>>
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>
> --
> To learn more about my evaluation book go to:
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> My personal webpage:
> http://www-hsc.usc.edu/~tvalente/
> ---
> Thomas W. Valente, PhD
> Director, Master of Public Health Program
> http://www.usc.edu/medicine/mph/
> Department of  Preventive Medicine
> School of Medicine
> University of Southern California
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>
> _____________________________________________________________________
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A.J. Rossini
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http://www.analytics.washington.edu/

_____________________________________________________________________
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