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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,
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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