The effect of density on the level of bias in the network autocorrelation model

Mark S. Mizruchi, Eric J. Neuman

Research output: Contribution to journalArticlepeer-review


Researchers interested in the effects of social network ties on behavior are increasingly turning to the network autocorrelation model, which allows for the simultaneous computation of individual-level and network-level effects. Earlier research, however, had pointed to the possibility that the maximum likelihood estimates used to compute the network autocorrelation model yielded negatively biased parameter estimates. In this paper we use simulations to examine whether - and the conditions under which - a negative bias exists. We show that the network parameter estimate ρ is negatively biased under nearly all conditions, and that this bias becomes more severe at higher levels of both ρ and network density. We conclude by discussing the implications of these findings for researchers planning to use the network autocorrelation model.

Original languageEnglish (US)
Pages (from-to)190-200
Number of pages11
JournalSocial Networks
Issue number3
StatePublished - Jul 2008


  • Network autocorrelation model

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • Social Sciences(all)
  • Psychology(all)


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