ScholarGate
Asisten
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.

Buka di MethodMindSegeraTerapkan, bandingkan, dapatkan panduan
Alat & sumber daya
Unduh salindia
Belajar & jelajahi
VideoSegera

Baca metode selengkapnya

Khusus anggota

Masuk dengan akun gratis untuk membaca bagian ini.

Masuk

Peta metode

Lingkup metode terkait — pilih sebuah simpul untuk menjelajah.

Sumber

  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

Cara menyitasi halaman ini

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

Metode yang mana?

Letakkan metode ini berdampingan dengan kerabat terdekatnya dan baca secara bersisian — pustaka menata bukunya di atas meja; pilihan ada di tangan Anda.

Bandingkan berdampingan

Dirujuk oleh

ScholarGateNetwork Autocorrelation Model (Network Autocorrelation Model of Social Influence). Diakses 2026-06-24 dari https://scholargate.app/id/sociology/network-autocorrelation-model · Set data: https://doi.org/10.5281/zenodo.20539026