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Abstract:
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In this paper, I introduce new methods for multilevel network analysis. The new methods can combine results from multiple network models, assess the effects of predictors at network or higher levels and account for both within- and cross-network correlations of the parameters in the network models. To demonstrate the new methods, I studied the effects on friendship networks of a smoking prevention intervention that was implemented in six middle schools involving over 3,400 students. The results show that as compared to random intervention (i.e., that targeted random students), smokers' popularity was significantly reduced in the classes with network interventions (i.e., those targeted central students or students with their friends together). The findings highlight the importance of examining network outcomes in evaluating social and health interventions, the role of social selection in managing social influence, and the potential of using network methods to design more effective interventions.
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