Multilevel Models for Multilevel Networks
Mark Tranmer (University of Glasgow)
Friday 21st October, 2016 15:00-16:00 Maths 203
I explain how a type of multilevel (mixed) model called a Multiple Membership Multiple Classification (MMMC) model can be used to investigate multilevel network dependencies for a nodal dependent variable at the lowest level of such a structure. In particular, the MMMC model allows estimation of the relative share of variation in the dependent variable across the various components of a multilevel network in which it is embedded. The idea is illustrated in this presentation with a case study: an analysis of the French cancer researchers multilevel network data, collected by Lazega et al. in 2008. This dataset includes ties between individual researchers (the level 1 network), and ties between the laboratories in which they work (the level 2 network), as well as the affiliations of researchers (level 1 nodes) to laboratories (level 2 nodes). The dependent variable in the case study is an interval-scale performance score for each researcher, which is a level 1 network nodal variable. The results suggest that network variation in the performance of the researchers is particularly associated with the way in which researchers nominate other researchers (their outgoing ties), and that some characteristics of the researchers are associated with differences in research performance, in particular the speciality of the researcher. I conclude with some general comments about the research value of the MMMC for multilevel networks, and briefly discuss further extensions to the model.