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Dear all,

First, thank you all for the comments (by Jacob, Filip, Tom, Carter, Dave &
Johan), they have been quite helpful both in terms of better understanding
the analytic logic(s) of ERGMís and how I could (maybe alternatively) go
about my research.

The distinction between *holistic* and *element-wise* conceptions of an ERGM
that Carter elaborated has been most informative (Iím not familiar with
classical mechanics, but in my past life I did some ecosystem energy-flow
modeling, so I do relate to the notion of a series of equations
stochastically describing the potential function of a system).  Apart from
section 3 of the paper on ERGM terms Dave suggested I donít believe I had
come across any explicit statement of why the usual dependent/independent
variable interpretation can be misleading.  I believe a published
elaboration of these notions would be a very helpful and welcome addition to
the ERGM literature, which to this point has been mostly of a technical
nature Ėfor understandable reasons.

Based on the above, I think I now have a better idea of when it would be
advisable to employ an ERGM on my own research.  There are a couple of my
research interests that would indeed benefit from an ERGM since I do want to
model the whole network.  The particular question I had in mind when I wrote
my post to the forum, however, I now believe would be better addressed by
some form of a network autocorrelation model (Iíll have to give it some
thought Ėsome of the node attributes in which Iím interest are dichotomous,
some continuous. And being an anthropologist, I do have ethnographic
information ;-) ).

Again, thanks to all for the help

 

Jorge.

 

 

From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
Behalf Of Johan Koskinen
Sent: Tuesday, May 03, 2011 4:03 AM
To: [log in to unmask]
Subject: Re: [SOCNET] ergm question

 

***** To join INSNA, visit http://www.insna.org ***** 

Jorge,

To further add to previous comments, it may be good to keep in mind two
things. Firstly, the method should mirror the substantive issue. If you are
interested in explaining nodal attributes you should use a model for nodal
attributes and not a model, like ERGM, for tie-variables. This is much in
the same way that if you are interested in income differences for men and
women you would regress income on gender and not the other way around (i.e.
regressing gender on income). Secondly, if you want to explain attributes,
you model attributes of, say, n individuals. If you want to explain
tie-variables, you are modelling (in the undirected case) n times n-1
tie-variables. Consequently, if you draw inference about your covariates
based on an ERG model for tie-variables, in a sense you are using your actor
attributes n-1 times. You may for example have 25 homophilous tied pairs of
actors for a binary attribute (for or against) but only 10 individuals in
total. An ERGM would treat the homophilous (and heterophilous, naturally)
ties as observations.

In addition to the techniques mentioned previously (QAP and network
effects/autocorrelation models), an attribute model analogous to the ERGM
for tie-variables is derived in Robins, G., Pattison, P., & Elliot, P.
(2001). Network models for social influence processes. Psychometrika, 66 ,
161Ė190. (A related model is described and elaborated in a chapter of a book
on ERGM that is forthcoming)

j. 

 

The email [log in to unmask] WILL EXPIRE on 30 April 2011

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Humanities Bridgeford Street
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johan.koskinen[at]manchester.ac.uk

  _____  

From: Social Networks Discussion Forum [[log in to unmask]] on behalf of
Garry Robins [[log in to unmask]]
Sent: 03 May 2011 04:57
To: [log in to unmask]
Subject: [SOCNET] FW: ergm question

***** To join INSNA, visit http://www.insna.org ***** 

Jorge,

 

There is nothing wrong with what you are doing. But just as with inferring
causality from a standard regression, you canít simply infer that the
ďpattern of social interactions influences the type of opinion individuals
haveĒ.  What you can say, though, is that there is an association between
the attribute (opinions) and network structure. This is all we can do with
cross-sectional data. It may or may not be enough for your research
purposes. Of course, if you are specifically investigating influence
hypotheses, then it makes sense to use an influence model rather than a
selection model Ė and Tom gives some good suggestions. Iím opposed to using
simple regressions here, Tom is right to point to the issue of dependency.

 

BTW there is an ERGM-based influence-type model in the pnet suite of
programs, akin to a logistic regression regression but taking into account
the network dependencies. It needs a binary attribute, however.

 

Good luck with the research

 

Garry

 

 

Dr Garry Robins

Associate Professor and Reader
Psychological Sciences
University of Melbourne
Victoria 3010
Australia

Tel: 61 3 8344 4454
Fax: 61 3 9347 6618
Web: http://www.psych.unimelb.edu.au/people/staff/RobinsG.html
Melnet website: http://www.sna.unimelb.edu.au/



  

 

  _____  

From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
Behalf Of Thomas Valente
Sent: Tuesday, 3 May 2011 4:32 AM
To: [log in to unmask]
Subject: Re: ergm question

 

All

I think Filipís suggestions are quite helpful and at the same time Jorgeís
frustrations understandable.  The problem is some people will be quite
concerned about network dependencies and the challenges with specifying
network effects whereas others will be content to focus on behaviors and
policy interpretations of these types of influences.  (Somewhat like the
balance between Type I and Type II error.)  And the researcher wishes to ďdo
the right thing.Ē  We/I have opted to do both, node level regressions using
somewhat standard multi-level (or hierarchical) regression techniques and,
when possible, build models using STATNET and SIENA.  So far the results are
fairly consistent with different approaches yielding different but not
contradictory insights.  An example is our paper on the adoption/diffusion
of the FCTC:

Wipfli, H., Fujimoto, K., & Valente, T. W. (2010).  Global tobacco control
diffusion: The case of the framework convention on tobacco control. American
Journal of Public Health, 100, 1260-1266.

So there is no right answer and no single approach that covers all
situations.  The researcher will need to pursue at least 2 and sometimes
multiple modeling approaches to be confident the findings are consistent
across multiple specifications.

-Tom

 

Thomas W. Valente, PhD

Current:  …cole des hautes ťtudes en santť publique (Rennes/Paris, France)

Usual:

Director, Master of Public Health Program
http://www.usc.edu/medicine/mph/

Professor, Department of Preventive Medicine

Keck School of Medicine

University of Southern California

1000 S. Fremont Ave., #8

Building A Room 5133                      

Alhambra CA 91803               

phone: (626) 457-4139; cell: (626) 429-4123

email: [log in to unmask]

 

Social Networks and Health: Models, Methods, and Applications:

        <http://www.oup.com/us> http://www.oup.com/us (promo code: 28569)

Evaluating Health Promotion Programs: www.oup-usa.org/

Network Models of the Diffusion of Innovations: www.hamptonpress.com

My personal webpage: http://www-hsc.usc.edu/~tvalente/

The Empirical Networks Project: http://ipr1.hsc.usc.edu/networks/

You Tube video on Diffusion of Innovations:
http://www.youtube.com/watch?v=ZG9dAIBd4xQ

 

From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
Behalf Of F.Agneessens
Sent: Monday, May 02, 2011 7:58 PM
To: [log in to unmask]
Subject: Re: ergm question

 

***** To join INSNA, visit http://www.insna.org ***** Dear Jorge,

If you are interested in explaining individual outcome, why not use a
regression model with these opinions as dependent variables and network
structure as independent? 
This has been done quite extensively: e.g., 
- Brass, D.J. 1984. Being in the right place. A structural analysis of
individual influence in an organization. Administrative Science Quarterly
29: 518-539.
- Sparrowe, R.T, Liden, R.C., Wayne, S.J., Kraimer, M.L. 2001. Social
networks and the performance of individuals and groups. Academy of
Management Journal: 316-325.

If you do not want to rely on the significance test of the 'normal'
regression (which traditionally relies on a random sampling from a
population), you could even make use of a QAP-like node level regression.
For example in UCINET you can do this with the following command:
Tools>Testing hypotheses>Node Level>Regression.

However, if you think a person's opinion is influenced by the opinions of
his connections you can use an 'autocorrelation'-type of regression. In that
case, see for example:
-Doreian, P. 1981. Estimating linear models with spatially distributed data.
Sociological Methodology 12:359-388.
-Doreian, P., K. Teuter, C. Wang. 1984. Network autocorrelation models: Some
Monte Carlo results. Sociological Methods & Research 13: 155-200.
-Marsden, P.V., Friedkin, N.E. 1993. Network studies of social influence.
Sociological Methods & Research 22, 127-151.
-Leenders, R.Th.A.J. 2002. Modelling social influence through network
autocorrelation: Constructing the weight matrix. Social Networks 24: 21-47.

Hope this helps,
Filip


On 02-05-11, Jorge M Rocha <[log in to unmask]> wrote:

***** To join INSNA, visit http://www.insna.org ***** 


Dear all,

I have a conceptual question that is probably somewhat naÔve regarding the
analytic logic behind ERGMís (Iíve been using statnet and its suite of
tools).  If someone in the community could point me in the right direction
literature wise, I would appreciate it.  Now let me try to state my
question/doubt as briefly and clearly as I can:

From what I understand, the modeling framework of an ERGM treats the
observed network as the dependent variable and the specified structural
configurations and covariate information about nodes/edges as independent
variables whose presence/absence increases the log-likelihood for the model.
In other words, what we want to achieve in these models is to gain a better
understanding of the types of processes that might have gone into generating
a network similar to the observed one; hypotheses are about this or that
configuration or node/edge attribute and their effect on the pattern of
social interactions represented in the network. If so, how would I go about
testing hypotheses concerning the effects of social interactions on nodal
attributes (Iím particularly thinking of different types of opinions
individuals might tend to have based on their interactions)?   

What Iíve done so far is kind of arguing backwards (or at least that is how
it feels to me Ėmaybe because Iím coming from a traditional linear/logit
regression background, and probably part of my problem is thinking in terms
of DV and IV).  If the coefficients are significant for some node covariate
of my interest in the ERGM estimation, then Iíve been interpreting this as
evidence that it is not unreasonable to argue that the pattern of social
interactions influences the type of opinion individuals have (I know that if
I had longitudinal data this issue of reverse causality would not be so much
of an issue, but so far all I have is cross-sectional data).  How wrong am I
on this (the backward-arguing)?

Finally, part of my worry is that Iíll be sending the research to a consumer
behavior/marketing journal where the familiarity with social network stuff
Ėlet alone ERGMísóis limited.  Any suggestions on how to best explain these
issues to a non-specialized audience?

In advance, thanks,

 

Jorge M Rocha

EGADE Business School

 

 

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-- 

 

Dr. Filip Agneessens

Department of Sociology/ICS

University of Groningen

Grote Rozenstraat 31

9712 TG Groningen

The Netherlands

email:  <mailto:[log in to unmask]> [log in to unmask]

 <http://filipagneessens.wordpress.com/>
http://filipagneessens.wordpress.com/

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researchers (http://www.insna.org). To unsubscribe, send an email message to
[log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of
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researchers (http://www.insna.org). To unsubscribe, send an email message to
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