*****  To join INSNA, visit  *****


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.



Thomas W. Valente, PhD

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


Director, Master of Public Health Program

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: (promo code: 28569)

Evaluating Health Promotion Programs:

Network Models of the Diffusion of Innovations:

My personal webpage:

The Empirical Networks Project:  <>

You Tube video on Diffusion of Innovations:


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 ***** 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,

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

***** To join INSNA, visit ***** 

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]


_____________________________________________________________________ SOCNET
is a service of INSNA, the professional association for social network
researchers ( To unsubscribe, send an email message to
[log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of
the message. 

SOCNET is a service of INSNA, the professional association for social
network researchers ( To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.