Multilevel Meta Network Analysis

• In Event: 204 - Regular Session. Quantitative Methodology

Abstract

In this paper, I introduce the latest advances in meta analysis for multilevel research on multiple networks. The new methods can not only combine results from multiple network models but also assess the effects of network level or higher levels of predictors. The new methods can also account for both within- and cross-network correlations of the parameters in the network models. To demonstrate the new methods, I applied them to studying network dynamics of a smoking prevention intervention that was implemented in 76 classes of six middle schools in China. A quarter of random students, central students (i.e., those with a lot friends), and students with their friends as a group were selected, respectively, from comparable classes to participate in the intervention. The results show that as compared to the classes with the random intervention (i.e., that targets random students), smokers' popularity was significantly reduced in the classes with network interventions (i.e., those target central students or students with their friends together). The effect is probably more robust for the central intervention (i.e., that targets central students) than for the group intervention. The findings highlight the importance of examining network outcomes in evaluating social and health interventions and also the role of social selection in managing social influence.

Author

• Weihua An, Indiana University