Introduction to social network analysis methods

This chapter explores Social network analysis (SNA) that enables public administration researchers to answer questions about the structure of networks, both intra- and inter-organizational, that traditional statistical methods cannot. This chapter introduces the benefits and drawbacks of SNA; the conceptual language of social networks; and provides an introduction to the method, including data collection, storage of data in matrices, and data visualization via sociograms. The concepts and measurement of network density, clustering, node centrality, and homophily are also introduced. Although SNA can be used to answer questions at the level of the dyad, such as the likelihood of a trust tie being present between two network members, this chapter focuses on whole network research. SNA at the whole-network level has been used to understand how the structure of relationships, including the structure of governance, influences network effectiveness. Drawing upon the network governance models of Provan and Kenis (2007), this chapter uses data collected from two local kindergarten readiness networks to make a recommendation about how they may merge and be governed.