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I have a conceptual question that is probably somewhat na´ve regarding the analytic logic behind ERGM’s (I’ve 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 (I’m particularly thinking of different types of opinions individuals might tend to have based on their interactions)?
What I’ve done so far is kind of arguing backwards (or at least that is how it feels to me –maybe because I’m 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 I’ve 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 I’ll be sending the research to a consumer behavior/marketing journal where the familiarity with social network stuff –let alone ERGM’s—is limited. Any suggestions on how to best explain these issues to a non-specialized audience?
In advance, thanks,
Jorge M Rocha