Christophe is quite right, my procedures were more appropriate for ego-centric data, not sociometric. I think in this case Paul has sociometric data and so estimates of in-degree, out-degree, and reciprocity, as well as other network properties can be made. - Tom
"Van den Bulte, Christophe" wrote:
> David Kenny has a paper that applies multilevel modeling to estimate a p1-type model for a continuous dyadic relatonship. Just like with p1, the model controls for sender emissiveness, AND recipient attractiveness, AND the tendency to reciprocate. I do not believe that the procedures mentioned by Tom deliver these benefits.
> The statistical implementation uses a multilevel model with "crossed effects," and can be estimated in SAS proc mixed. Any other canned software that allows for crossed effects in a multilevel model should be appropriate.
> I believe the paper is the folowing (sorry, I can't check my files right now):
> Snijders, Tom A. B; Kenny, David A. The social relations model for family data: A multilevel approach. Personal Relationships. Vol 6(4) Dec 1999, 471-486.
> Christophe Van den Bulte
> Assistant Professor of Marketing
> The Wharton School
> University of Pennsylvania
> 1472 Steinberg Hall-Dietrich Hall
> 3620 Locust Walk
> Philadelphia, PA 19104
> Tel: 215-898-6532
> Fax: 215-898-2534
> [log in to unmask]
> -----Original Message-----
> From: Thomas W. Valente [mailto:[log in to unmask]]
> Sent: Friday, April 19, 2002 1:07 PM
> To: [log in to unmask]
> Subject: Re: statistics help
> Paul and others,
> The advice I've been given by many statisticians is to use GEE (General
> Estimating Equations) with no constraints on the correlation matrix
> (expectations of the degree of correlation within dyads). The other strategy
> has been to use the Sandwhich Estimator (Huber-White), both return similar
> results. In our paper on syringe sharing among needle exchange participants we
> had a cohort sample with egocentric network data. The cohort was uneven in that
> respondents varied in the number of followup interviews they had completed. We
> reshaped the data to be dyadic giving non-independence at 2 levels, the number
> of interviews and network. (Theoretically we might have been able to specify
> more covariation within respondents compared to within survey times, but
> mathematically this has not yet been implemented in any statistical package that
> I know of.) You can also use a general Multi- level model framework (also known
> as random effects model) specificing co-variation within respondents. Depending
> on the statistical package, someone can provide model examples (I use STATA
> mostly now).
> Statisticians may provide better and more complete answers. - Tom V.
> Paul Chung wrote:
> > Hi! As a network novice, I've run into a problem that I imagine most
> > socnetters have already successfully handled.
> > I performed an analysis of the survey responses of 52 subjects, whom I
> > assorted into N*(N-1)/2 = 1326 unique dyads. My response variable was the
> > dyadic agreement in survey answers (measured on a scale), which I put into
> > an ordered logit regression.
> > The problem, of course, is that these dyads, while unique, are not
> > independent, so my standard errors are wrong. Does anyone have an easy
> > solution to this problem?
> > My e-mail address is below. If you feel that the question is of general
> > interest, please feel free to post your response.
> > Thanks! Any help at all would be appreciated. I look forward to hearing from
> > you.
> > Sincerely,
> > Paul Chung
> > Email: [log in to unmask]
> To learn more about my evaluation book go to:
> Thomas W. Valente, PhD
> Director, Master of Public Health Program
> Department of Preventive Medicine
> School of Medicine
> University of Southern California
> 1000 Fremont Ave.
> Building A Room 5133
> Alhambra CA 91803
> phone: (626) 457-6678
> fax: (626) 457-6699
> email: [log in to unmask]
To learn more about my evaluation book go to:
Thomas W. Valente, PhD
Director, Master of Public Health Program
Department of Preventive Medicine
School of Medicine
University of Southern California
1000 Fremont Ave.
Building A Room 5133
Alhambra CA 91803
phone: (626) 457-6678
fax: (626) 457-6699
email: [log in to unmask]