Advances in Data Analysis and Classification

, Volume 5, Issue 2, pp 147–176

Assessing and accounting for time heterogeneity in stochastic actor oriented models

  • Joshua A. Lospinoso
  • Michael Schweinberger
  • Tom A. B. Snijders
  • Ruth M. Ripley
Regular Article

Abstract

This paper explores time heterogeneity in stochastic actor oriented models (SAOM) proposed by Snijders (Sociological methodology. Blackwell, Boston, pp 361–395, 2001) which are meant to study the evolution of networks. SAOMs model social networks as directed graphs with nodes representing people, organizations, etc., and dichotomous relations representing underlying relationships of friendship, advice, etc. We illustrate several reasons why heterogeneity should be statistically tested and provide a fast, convenient method for assessment and model correction. SAOMs provide a flexible framework for network dynamics which allow a researcher to test selection, influence, behavioral, and structural properties in network data over time. We show how the forward-selecting, score type test proposed by Schweinberger (Chapter 4: Statistical modeling of network panel data: goodness of fit. PhD thesis, University of Groningen 2007) can be employed to quickly assess heterogeneity at almost no additional computational cost. One step estimates are used to assess the magnitude of the heterogeneity. Simulation studies are conducted to support the validity of this approach. The ASSIST dataset (Campbell et al. In Lancet 371(9624):1595–1602, 2008) is reanalyzed with the score type test, one step estimators, and a full estimation for illustration. These tools are implemented in the RSiena package, and a brief walkthrough is provided.

Keywords

Stochastic actor oriented models Longitudinal analysis of network data Time heterogeneity Score-type test 

Mathematics Subject Classification (2000)

62M02 

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Joshua A. Lospinoso
    • 1
    • 2
  • Michael Schweinberger
    • 3
  • Tom A. B. Snijders
    • 1
    • 4
  • Ruth M. Ripley
    • 1
  1. 1.Department of StatisticsUniversity of OxfordOxfordUK
  2. 2.Network Science CenterUnited States Military AcademyNew YorkUSA
  3. 3.Department of StatisticsPennsylvania State UniversityUniversity ParkUSA
  4. 4.Department of SociologyUniversity of GroningenGroningenThe Netherlands

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