Multilevel analysis of personal networks as dependent variables

Abstract In this paper, it is shown that multilevel methods are particularly well-suited for the analysis of relations in personal networks and the changes in these relations. Justice is done to the hierarchical nested structure of the data and the resulting dependence between observations “within egos”. Multilevel techniques can also give more specific insight on why personal networks change: they allow to distinguish between the influence of individual and of tie characteristics on the stability of personal networks as a whole and of specific ties within a personal network. This is illustrated by an application to changes in networks of four Dutch samples experiencing different life events.

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