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5 December 2005

Longitudinal data, causal inferences, and the institutional milieu

A quick follow up on my earlier post re causal inferences, in which I stated that longitudinal data are not a cure all for determining the direction of the causal arrow. Longitudinal data, in principle, should allow a tracing of what preceded what temporally, and thus (hopefully) causally. Thus, in the context of social influence, if A and B started talking at time t, and their attitudes converged at time t + 1, it would seem reasonable to assert that communication lead to convergence (social influence), rather than prior similarity to communication (homophily). However, it possible that exogenous factors are dynamically operating on either the network or attitudes (to take the social influence example) over time. For example, imagine the attitude in question has to do with the role of government in markets, and one found looking at a population that both attitudes and communication patterns converged over time (suggesting both homophily and social influence). But now add to this scenario that the population in question is an undergraduate cohort, and an alternative explanation might simply be that ones major (e.g., economics) affects both attitudes and ties over time. It is plausible that such a process would lead to incorrect inferences regarding the sources of attitudes and ties. More generally, institutions affect both outcomes of interest and the configuration of networks. Neglect of the institutional milieu (which is often ignored in SNA research) can thus lead to spurious inferences regarding the reciprocal influence of networks and individual-level outcomes, even with longitudinal data.

Posted by David Lazer at December 5, 2005 1:58 PM

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