Abstract:

Our contacts with other people can shape our view of the world, reinforce our identity, and the interactions provide us with all kinds of opportunities and resources to get things done. Traditional research mainly uses attribute data which reflect individual’s attitude, viewpoints and behaviors to study the relation. Social network analysis (SNA) is a new approach which focuses on the connections among social entities, especially the relational links and structures which were neglected in the traditional relation research. It insists that social context contains social ties and connections, not the total of different units simply. SNA uses relational data rather than attribute data to study the relation. Relational data are the contacts, ties, connections which relate one person to another and can’t be reduced to the properties of the individual themselves. SNA provides the means to derive a more complete view of a given social environment. This paper reviewed some functions of social network analysis in the view of psychology. Fundamental functions of social network analysis include centrality analysis which can reflect one’s status and power, clique analysis which can assign individuals to subgroups, position analysis which can find the similar status or power’s sets, and QAP which is the method of correlation and regression analysis to explain the relationships of different relational matrixes. Furthermore, SNA can also analyze the relationships between attribute data and relational data by the statistical models. Until now, the models mainly includes p1 model (loglinear model) which allows us to detect the dyadic directed relation, p2 model (random effects model) which allows us to study the attribute covariates of nominators and their targets between dyads, and p* model (exponential random graph model) which allows us to study network structure and the attribute covariates. UCINET and StOCNET are the two main soft wares to execute these analyses. The field of social network analysis is extending and growing, and new methods and approaches are constantly in development. SNA has been widely used in the sociology field and begin to be introduced in psychology recently. This paper is a general introduction to social network analysis, aiming to provide some indications to Chinese psychology researchers.

Key words: social network analysis, relational data, clique Analysis, p* model

摘要：

社会网络分析（Social Network Analysis, SNA）是用社会实体之间的关系来描述、解释和预测社会现象的一种研究取向。SNA提供了深入探究社会环境特征及其对个体心理发展影响的一种方法。本文基于SNA的发展历程，依次介绍了中心性分析、小团体分析、位置分析、QAP以及统计模型法。SNA在社会学研究中得到了较多应用，近年来在心理学研究中开始受到重视。

关键词: 社会网络分析, 关系数据, 小团体分析, P*模型