ScholarGate
助手
Regression modelSocial influence / peer effects modeling

Network Autocorrelation Model

The network autocorrelation model adapts spatial-econometric regression to social networks to estimate peer influence: it explains an actor's outcome — an attitude, behavior, or performance — as a function of their own covariates plus a weighted average of their network partners' outcomes. The autocorrelation parameter ρ captures the strength of social influence, and the network weight matrix W encodes who influences whom and how strongly.

在 MethodMind 中打开即将推出应用、比较、获取指导
工具与资源
下载幻灯片
学习与探索
视频即将推出

阅读完整方法

仅限会员

使用免费账户登录即可阅读本节。

登录

方法图谱

相关方法的邻域——选择一个节点以展开探索。

来源

  1. Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI: 10.1016/S0378-8733(01)00049-1
  2. Doreian, P. (1980). Linear models with spatially distributed data: Spatial disturbances or spatial effects? Sociological Methods & Research, 9(1), 29–60. DOI: 10.1177/004912418000900102

如何引用本页

ScholarGate. (2026, June 22). Network Autocorrelation Model of Social Influence. ScholarGate. https://scholargate.app/zh/sociology/network-autocorrelation-model

选用哪种方法?

将本方法与其最相近的同类并置,并排研读——本馆将书籍铺陈于案上,取舍则由您定夺。

并排比较

被引用于

ScholarGateNetwork Autocorrelation Model (Network Autocorrelation Model of Social Influence). 于 2026-06-24 检索自 https://scholargate.app/zh/sociology/network-autocorrelation-model · 数据集: https://doi.org/10.5281/zenodo.20539026