Acta Psychologica Sinica ›› 2014, Vol. 46 ›› Issue (12): 1933-1945.doi: 10.3724/SP.J.1041.2014.01933
JIAO Can1; WU Huanjie1; HUANG Yuena1; HUANG Feifei2; ZHANG Minqiang2
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.
network autocorrelation model,
social network analysis,
JIAO Can; WU Huanjie; HUANG Yuena; HUANG Feifei; ZHANG Minqiang. (2014). Application of Network Autocorrelation Models in Psychological Studies: Taking the Impact of Peer Effect, Learning Motivation on Adolescents’ Academic Performance as Examples. Acta Psychologica Sinica, 46(12), 1933-1945.
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