Applications of New Techniques of Data Gathering and Statistical Analysis for Social Networks

Abstract

© 2013 The Authors. Published by Elsevier Ltd. Social Network Analysis (SNA) has its origins in psychology, statistics and mathematics and, apart from these is applied in as diverse disciplines as sociology, management sciences, epidemiology, economics, and political science. Contrary to other analytical tools that mainly concentrate on attributes of cases for the explanation of given phenomena, SNA focuses on network relations between cases. A central assumption of SNA is that these relations and interdependencies matter for the explanation of individual or collective behaviour and outcomes. SNA provides powerful tools for the description and illustration of network structures and has for a long time been successfully applied to that purpose. In recent years, SNA has attracted an increased interest among scholars and has gained prominence through major publications. Two reasons account for this evolvement: first, the development of statistical models for network data has increased the number of methodological techniques network data can be analysed with. Indeed, the main strength of SNA, i.e. its focus on interdependencies among cases, has been (and still is) a major challenge for statistical analysis, which ideally relies on independent observations. Second, network data is increasingly available to scholars, as new methods of . This is particularly true with respect to data on relations among individuals that can be grasped relying on new social media. The Conference on Applications of Social Network Analysis (ASNA) tries to take stock of these rapid developments. Its 9