ISSN 0439-755X
CN 11-1911/B

心理学报 ›› 2014, Vol. 46 ›› Issue (12): 1933-1945.doi: 10.3724/SP.J.1041.2014.01933

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网络自相关模型在心理学研究中的应用 ——以同群效应、学习动机对青少年学业表现的影响为例


  1. (1深圳大学师范学院心理学系, 深圳 518060) (2华南师范大学心理应用研究中心/心理学院, 广州 510631)
  • 收稿日期:2014-03-05 发布日期:2014-12-25 出版日期:2014-12-25
  • 通讯作者: 张敏强, E-mail:
  • 基金资助:


Application of Network Autocorrelation Models in Psychological Studies: Taking the Impact of Peer Effect, Learning Motivation on Adolescents’ Academic Performance as Examples

JIAO Can1; WU Huanjie1; HUANG Yuena1; HUANG Feifei2; ZHANG Minqiang2   

  1. (1 Psychology Department, Shenzhen University, Shenzhen 518060, China) (2 Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China)
  • Received:2014-03-05 Online:2014-12-25 Published:2014-12-25
  • Contact: ZHANG Minqiang, E-mail:


用常规的线性模型来处理彼此之间并不独立的关系数据, 会违背线性模型关于独立性的假设。引入网络自相关模型, 在介绍其发展及原理的基础上, 通过模拟研究比较网络效应模型和常规线性模型在处理关系数据时的差异。结果表明, 在关系数据的参数估计和拟合程度上, 网络效应模型都显著优于常规线性模型, 宜采用网络效应模型来处理关系数据。最后以同群效应、学习动机对青少年学业表现的影响为例, 探讨该模型在心理学研究中应用的可能性和必要性。

关键词: 网络自相关模型, 关系数据, 社会网络分析, 同群效应


In general, social science data can be divided into attribute data and relational data. Focusing on individual properties, plus lagged statistical methods, the traditional social research, by simplifying relational data into attribute data, adopts traditional statistical analysis to deal with the former. This approach is not desirable, because traditional statistical analysis needs to meet the independence of cases. However, relational data mainly involves the relationships between interdependent actors, in which sense, it violates the assumption of independence, inapplicable to traditional statistical analysis. With the development of the statistical methods, a new approach—social network analysis (SNA) is proposed to deal with relational data. Social network analysis is a large and growing body of researches on the measurement and analysis of relational structure. It mainly evaluates relationships between actors, and the contexts of the social actors. Network autocorrelation models are common for social network analysis, which are used to study on the relationship between network effect and individual behavior. In order to explore the difference between social network analysis and traditional statistical analysis, we have compared the performance of network effect model and traditional linear model in dealing with relational data through simulation studies. The simulation studies were conducted in R statistical programming environment. This article also presents the application of network effect model in psychology, and the empirical study was to investigate the impact of peer effect and learning motivation on adolescents’ academic performance. Network effect model, a type of the network autocorrelation models that fully considers the interdependencies among sample units, was applied to delve into the data by using “sna” software package in R project. The simulation study suggests that parameter estimation and model fit of network effect model are significantly better than traditional linear model in dealing with relational data —that’s why network effect model should be applied. The results of the empirical study reveal that peer effect has significant impact on academic performance. Overall, the findings not only highlight that social network analysis should be applied to deal with relational data, but also indicate that peer effect is crucial to adolescents’ academic performance.

Key words: network autocorrelation model, relational data, social network analysis, peer effect