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Related Experiment Video

Updated: Apr 16, 2026

Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
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Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based

John R Hipp1, Cheng Wang1, Carter T Butts1

  • 1Department of Criminology, Law and Society and Department of Sociology, University of California, Irvine.

Social Networks
|March 7, 2015
PubMed
Summary
This summary is machine-generated.

Missing network data in longitudinal analysis can alter study conclusions. Researchers using stochastic actor based models (SABMs) must carefully choose missing data strategies for accurate results.

Keywords:
SIENASmokingpeer influence substance use behaviorpeer selectionstochastic actor based model

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Area of Science:

  • Social network analysis
  • Longitudinal data analysis
  • Statistical modeling

Background:

  • Stochastic actor based models (SABMs) are increasingly used for longitudinal network data.
  • The impact of missing network data on SABM estimation remains understudied.

Purpose of the Study:

  • To investigate the consequences of different missing data strategies in longitudinal network analysis using SABMs.
  • To assess how missing data handling affects substantive conclusions from network models.

Main Methods:

  • Utilized data from four schools across three time points from the AddHealth dataset.
  • Compared four distinct strategies for addressing missing network data within SABMs.

Main Results:

  • Some model parameters were robust across different missing data strategies.
  • Substantive conclusions drawn from the models varied significantly depending on the chosen missing data strategy.

Conclusions:

  • The choice of missing data strategy critically impacts findings in longitudinal network analysis.
  • Researchers should exercise caution and carefully consider missing data approaches when applying SABMs.