scholarly journals Peer influence in the development of adolescent antisocial behavior: Advances from dynamic social network studies

2018 ◽  
Vol 50 ◽  
pp. 140-154 ◽  
Author(s):  
Jelle J. Sijtsema ◽  
Siegwart M. Lindenberg
Author(s):  
Tianbao Yang ◽  
Shenghuo Zhu ◽  
Yun Chi ◽  
Yihong Gong

2021 ◽  
Vol 45 (3) ◽  
pp. 275-288
Author(s):  
Dawn DeLay ◽  
Brett Laursen ◽  
Noona Kiuru ◽  
Adam Rogers ◽  
Thomas Kindermann ◽  
...  

The present study compares two methods for assessing peer influence: the longitudinal actor–partner interdependence model (L-APIM) and the longitudinal social network analysis (L-SNA) Model. The data were drawn from 1,995 (49% girls and 51% boys) third grade students ( M age = 9.68 years). From this sample, L-APIM ( n = 206 indistinguishable dyads and n = 187 distinguishable dyads) and L-SNA ( n = 1,024 total network members) subsamples were created. Students completed peer nominations and objective assessments of mathematical reasoning in the spring of the third and fourth grades. Patterns of statistical significance differed across analyses. Stable distinguishable and indistinguishable L-APIM dyadic analyses identified reciprocated friend influence such that friends with similar levels of mathematical reasoning influenced one another and friends with higher math reasoning influenced friends with lower math reasoning. L-SNA models with an influence parameter (i.e., average reciprocated alter) comparable to that assessed in L-APIM analyses failed to detect influence effects. Influence effects did emerge, however, with the addition of another, different social network influence parameter (i.e., average alter influence effect). The diverging results may be attributed to differences in the sensitivity of the analyses, their ability to account for structural confounds with selection and influence, the samples included in the analyses, and the relative strength of influence in reciprocated best as opposed to other friendships.


2014 ◽  
Vol 25 (10) ◽  
pp. 1450056 ◽  
Author(s):  
Ke-Ke Shang ◽  
Wei-Sheng Yan ◽  
Xiao-Ke Xu

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.


2015 ◽  
Vol 104 ◽  
pp. e7-e11 ◽  
Author(s):  
Alecia J. Carter ◽  
Alexander E.G. Lee ◽  
Harry H. Marshall

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