文章基本信息

标题：Applications of New Techniques of Data Gathering and Statistical Analysis for Social Networks
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作者：Manuel Fischer ; Manuel Fischer
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2013
卷号：79
页码：1-3
DOI：10.1016/j.sbspro.2013.05.052
语种：English
出版社：Elsevier
摘要：Social Network Analysis (SNA) has its origins in psychology, statistics and mathematics and, apart from theseis 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 casesfor the explanation of given phenomena, SNA focuses on network relations between cases. A central assumptionof SNA is that these relations and interdependencies matter for the explanation of individual or collectivebehaviour and outcomes. SNA provides powerful tools for the description and illustration of network structuresand has for a long time been successfully applied to that purpose. In recent years, SNA has attracted an increasedinterest among scholars and has gained prominence through major publications. Two reasons account for thisevolvement: first, the development of statistical models for network data has increased the number ofmethodological techniques network data can be analysed with. Indeed, the main strength of SNA, i.e. its focus oninterdependencies among cases, has been (and still is) a major challenge for statistical analysis, which ideallyrelies on independent observations. Second, network data is increasingly available to scholars, as new methods of. This is particularly true with respect to dataon relations among individuals that can be grasped relying on new social media.