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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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