Understanding influence in social networks: New methods for estimation and inference using Bayesian statistics
Social network research plays an important role to understand how persons influence each other on behaviors, opinions, and well-being. The main interest is the structure of social influence which is studied using the network autocorrelation model. Currently available methods for analyzing this model cannot be adequately used (1) to analyze small networks, (2) to analyze models with different subgroups, and (3) to test a specific order of influence in a network. This project resolves these limitations by developing one encompassing Bayesian framework that will allow social network researchers to answer important research questions that cannot be answered at this moment.
prof. dr. J.K. Vermunt, prof. dr. R.T.A.J. Leenders, dr. J. Mulder
1 June 2014 – 1 January 2018