Multilevel meta network analysis with application to studying network dynamics of network interventions |
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| Institution: | 1. Department of Management and Marketing, College of Business, North Dakota State University, Fargo, ND 58108, United States;2. Gatton Chair in Management, Department of Management, Gatton College of Business & Economics, LINKS Center for Social Network Analysis, University of Kentucky, United States;3. Department of Information Systems, Carroll School of Management, Boston College, United States;1. Department of Justice Studies, San Jose State University, USA;2. Indiana University Bloomington, USA;1. Department of Experimental Psychology, University of Oxford, Oxford, UK;2. Institute of Informatics and Telematics of CNR, Pisa, Italy;1. US Army Research Institute for the Behavioral and Social Sciences, USA;2. National Human Genome Research Institute, USA;1. Indiana University, Department of Sociology and Department of Statistics, 752 Ballantine Hall, 1020 E. Kirkwood Ave., Bloomington, IN 47405, USA;2. Indiana University, Department of Sociology, 744 Ballantine Hall, 1020 E. Kirkwood Ave., Bloomington, IN 47405, USA |
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| Abstract: | In this paper, I introduce new methods for multilevel meta 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 network dynamics of a smoking prevention intervention that was implemented in 76 classes of six middle schools in China. The results show that as compared to 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 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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| Keywords: | Multilevel model Meta network analysis Multivariate statistics Network intervention |
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