社会网络分析在心理研究中的应用

个体不是孤立存在的, 其心理与行为的变化常依赖于与他人存在的关系。社会网络分析正是考虑到了这种依赖性, 发展出相应的处理关系变量的方法。在介绍社会网络理论的中心思想、社会网络分析的基本概念、数据表示方法和研究方法以及在个体水平和关系水平上的静态统计模型后, 使用UCINET软件展现了处理关系数据的基本步骤, 并简述社会网络分析在人格心理学、发展与教育心理学和管理心理学中的应用。     

大学生网络成瘾的同伴传染：一项社会网络分析研究

以大二到大四475名大学生为被试,采用中文网络成瘾量表(CIAS-R)及同伴提名法测量其网络成瘾情况及人际关系网络特征指标,考察大学生网络成瘾的同伴影响。结果显示：(1)男生比女生更多居于同伴团体的中心位置,且与他人的“距离”更近;(2)标准化入接近中心度对个体网络成瘾有显著的负向预测作用,表明个体越处在人际网络中心,越不容易网络成瘾;(3)同伴团体网络成瘾对个体网络成瘾有显著正向预测作用。个体人际关系网络特征对网络成瘾的同伴传染调节效应不显著,即不论个体社交地位如何,网络成瘾的同伴影响不存在显著差异。由此推论,良好的同伴关系是个体网络成瘾的保护因素,网络成瘾存在同伴传染效应。     

学业成绩相似性与友谊对青少年寻求支持关系的影响：基于纵向社会网络分析

为了探讨青少年学业成绩相似性对寻求支持关系的影响,以及友谊在其中的作用,本研究采用间隔一年的追踪设计,以36个班1126名初中生为被试,通过纵向社会网络分析方法对数据进行分析。结果显示：（1）学业成绩相似的青少年之间更容易建立友谊和寻求支持关系;（2）学业成绩相似性对青少年寻求支持关系的建立既有直接的促进作用,也可以通过促进友谊而产生间接的促进作用。     

同伴团体对青少年问题行为的影响：一项基于社会网络分析的研究

以675名初中和高中学生为被试,采用社会网络分析方法,获得506名青少年在其班级中的网络中心度,并确定他们所属的同伴团体,在此基础上考察同伴团体的行为规范对其问题行为的影响。结果发现：（1）在控制了班级层次的问题行为水平和其他相关变量后,同伴团体的问题行为水平能够正向预测青少年自身的问题行为;（2）青少年在同伴团体内部的地位能负向预测青少年的问题行为,青少年在班级社交网络中的度数中心度能正向预测其问题行为,而中介中心度能负向预测其问题行为;（3）交互作用分析表明：同伴团体的问题行为水平主要对低中介中心度的青少年产生显著影响;仅在问题行为水平较高的同伴团体中,青少年的度数中心度正向预测其问题行为。     

青少年友谊网络与受欺凌的共同变化关系:一项纵向社会网络分析研究

欺凌是一种涉及多类行为和多主体互动的群体过程,但是已有研究很少在同伴的群体互动和关系变化过程中研究欺凌相关行为的发展。尤其是受欺凌作为一种被动承受而非主动选择的行为结果,对其在同伴情境中的变化过程研究更为有限。本研究从青少年的友谊网络与其受欺凌的共变关系入手,采用纵向社会网络分析法,对来自26个班1406名学生进行为期1年的3个时间点(七年级末、八年级上学期末、八年级下学期末)的追踪研究,结果发现:(1)受欺凌影响群体内友谊关系的建立:受欺凌水平高的青少年更难以与同伴建立朋友关系(同伴回避效应),且更倾向于选择同样受欺凌水平高的个体作为朋友(同伴选择效应);(2)友谊关系影响受欺凌水平的变化:在群体中拥有更多朋友关系的青少年,其受欺凌水平会逐渐降低(同伴保护效应);但另一方面,拥有高受欺凌水平朋友的青少年,自己的受欺凌水平会随着时间呈增高趋势(同伴影响效应);(3)随着时间的发展,女生的受欺凌水平比男生更容易降低(性别影响效应)。研究结果揭示了友谊网络和受欺凌的共变关系,为校园欺凌群体干预提供启示。     

青少年的同伴选择与同伴影响：基于学业投入与学业成就的纵向社会网络分析

以756名中学生为研究对象,对学业投入、学业成就、同伴团体进行追踪测量,探讨青少年同伴团体的学业投入及学业成就是否存在同伴选择与同伴影响效应。结果表明：（1）在1年的追踪时间中,青少年学业投入的发展呈下降趋势,初始水平与下降速度负相关,学业成就的发展呈上升趋势,初始水平与上升速度无显著相关;（2）学业投入与学业成就存在同伴选择效应,青少年倾向选择与自己学业投入及学业成就相似的个体构建同伴团体;（3）青少年的学业投入及学业成就与同伴逐渐趋近相似,即同伴影响效应显著。     

西方社会价值取向的研究历程与发展趋势

作为一种相对稳定的人格特质,社会价值取向指个体对于自己和他人结果分配的特定偏好。西方社会价值取向的研究经历了从双维模型到整合模型的发展。这些研究证实了实验室情境以及现实情境下社会价值取向对个体的行为决策和社会认知的影响。人际取向命题的提出是该领域的最新理论进展,情境因素和平等取向在未来研究中将得到更多的关注     

大学生价值取向与社会幸福感的关系研究

研究探讨了大学生价值取向与社会幸福感之前的关系。对400名大学生进行问卷调查,通过相关和回归分析,结果表明,大学生价值取向与社会幸福感之间存在显著的正相关,并且价值取向对社会幸福感有一定的预测作用。     

社会排斥与亲社会行为的关系：物质主义价值观与未来取向的作用

为考察社会排斥影响亲社会行为的心理机制及其边界条件,研究1采用cyberball范式操纵社会排斥,考察其对物质主义价值观和亲社会行为的影响;研究2采用回忆范式操纵社会排斥,并进一步考察了未来取向在社会排斥与亲社会行为的关系中的调节作用。研究结果表明：社会排斥可以负向预测亲社会行为,物质主义价值观在社会排斥对亲社会行为的预测中起部分中介作用,未来取向在社会排斥和亲社会行为之间起调节作用。研究结果揭示了社会排斥导致物质主义价值观水平升高,进而减少亲社会行为,而未来取向可以降低社会排斥对亲社会行为的负面影响。     

初中生利他取向、社会赞许性与亲社会行为关系的实验研究

通过对利他取向和社会赞许性两个维度的测试,将初中生被试区分为四种类型.借助对高、低赞许性情境的操纵,考察了四种类型被试在亲社会行为表现上的差异.结果表明,对亲社会行为的准确预测和解释要取决于利他取向、社会赞许性及情境三种因素的交互作用.社会赞许性变量的引入,加深了对高、低利他取向作用的认识,对指导初中生的思想品德教育有一定的意义.     

Social network analysis: A methodological introduction

Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single networks, network comparison, and analysis of individual-level covariates are discussed, and a number of suggestions are made for avoiding common pitfalls in the application of network methods to substantive questions.     

儿童欺负/受欺负与同伴网络的关系：p*模型分析

从四所小学的10个班中挑选出高欺负-受欺负班和低欺负-受欺负班,使用p^*模型分析班内朋友关系的网络结构特点及其与欺负、受欺负的关系。结果发现：（1）低欺负-受欺负班级中的同伴关系网络倾向于形成开放性朋友关系结构,如边、2-星、3-星、4-星结构;高欺负-受欺负班级中的同伴关系网络倾向于形成闭合性朋友关系结构,如三角形、孤立者结构;（2）高、低欺负-受欺负班级中,欺负者均处于同伴关系网络的边缘化位置,且所处的关系结构位置与自身欺负水平的关联不显著;（3）受欺负者所处的关系结构位置与自身受欺负水平的关联也不显著,但是在低欺负-受欺负班中,受欺负者倾向处于同伴关系网络的边缘化位置,而高欺负-受欺负班中,部分受欺负者处于边缘化位置,部分受欺负者处于闭合性关系结构的核心位置。     

I need to be your only friend: the effect of salesperson network centrality on opportunistic behavior

Transaction cost economics can be used to detect opportunistic behavior by one or more actors within a sales network. Using a sample of firms in the medical industry, where the role of salespeople is important, this study examines network centrality as a determinant of a salesperson’s opportunistic behavior in intrafirm relationships. The results indicate that opportunistic behavior is affected differently depending on a salesperson’s relational and positional centralities. While relational centrality decreases opportunistic behavior by providing motivation to protect one’s reputation, positional centrality negatively moderates the effect of relational centrality on opportunistic behavior. However, with a high relational centrality, a salesperson is less likely to be opportunistic when she or he is low in positional centrality than when her or his positional centrality is high.     

Centrality in children's best friend networks: The role of social behaviour

Centrality is an indicator of an individual's relative importance within a social group. Predictors of centrality in best friendship networks were examined in 146 children (70 boys and 76 girls, M_age = 9.95). Children completed measures of social confidence, social desirability, friendship quality, school liking, and loneliness and nominated their best friends from within their class at two time points, 3 months apart. Multigroup path analysis revealed gender differences in the antecedents of centrality. Social confidence, social desirability, and friendship quality predicted changes in the indicators of centrality in best friend networks over time. Boys' social behaviour positively predicted changes in centrality, whereas girls' social behaviour negatively predicted changes in centrality. Together, these findings suggest that some aspects of social behaviour are influential for centrality in best friend groups.     

Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp

Spanning nearly sixty years of research, statistical network analysis has passed through (at least) two generations of researchers and models. Beginning in the late 1930's, the first generation of research dealt with the distribution of various network statistics, under a variety of null models. The second generation, beginning in the 1970's and continuing into the 1980's, concerned models, usually for probabilities of relational ties among very small subsets of actors, in which various simple substantive tendencies were parameterized. Much of this research, most of which utilized log linear models, first appeared in applied statistics publications.But recent developments in social network analysis promise to bring us into a third generation. The Markov random graphs of Frank and Strauss (1986) and especially the estimation strategy for these models developed by Strauss and Ikeda (1990; described in brief in Strauss, 1992), are very recent and promising contributions to this field. Here we describe a large class of models that can be used to investigate structure in social networks. These models include several generalizations of stochastic blockmodels, as well as models parameterizing global tendencies towards clustering and centralization, and individual differences in such tendencies. Approximate model fits are obtained using Strauss and Ikeda's (1990) estimation strategy.In this paper we describe and extend these models and demonstrate how they can be used to address a variety of substantive questions about structure in social networks.This research was supported by grants from the Australian Research Council and the National Science Foundation (#SBR93-10184). This paper was presented at the 1994 Annual Meeting of the Psychometric Society, Champaign, Illinois, June 1994. Special thanks go to Sarah Ardu for programming assistance, Laura Koehly and Garry Robins for help with this research, and to Shizuhiko Nishisato and three reviewers for their comments. INTERNETemail addresses:pattison@psych.unimelb.edu.au (PP);stanwass@uiuc.edu (SW). Affiliations: Department of Psychology, University of Melbourne (PP); Department of Psychology, Department of Statistics, and The Beckman Institute for Advanced Science and Technology, University of Illinois (SW).     

Social networks with two sets of actors

Traditional network research analyzes relational ties within a single group of actors: the models presented in this paper involve relational ties exist beteen two distinct sets of actors. Statistical models for traditional networks in which relations are measured within a group simplify when modeling unidirectional relations measured between groups. The traditional paradigm results in a one-mode socionatrix; the network paradigm considered in this paper results in a two-mode socionatrix; A statistical model is presented, illustrated on a sample data set, and compared to its traditional counterpart. Extensions are discussed, including those that model multivariate relations simultaneously, and those that allow for the inclustion of attributes of the individuals in the group.We thank the Editor and two anonymous reviewers for their helpful comments. We are also grateful to George Barnett for allowing us to analyze his data.