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Assessing and accounting for time heterogeneity in stochastic actor oriented models

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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.

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References

  • Amemiya T, Nold F (1975) A modified logit model. Rev Econ Stat 57: 255–257

    Article  Google Scholar 

  • Audrey S, Cordall K, Moore L, Cohen D, Campbell R (2004) The development and implementation of an intensive, peer-led training programme aimed at changing the smoking behaviour of secondary school pupils using their established social networks. Health Educ J 63(3): 266–284

    Article  Google Scholar 

  • Basawa I (1985) Neyman-Le Cam tests based on estimation functions. In: Proceedings of the Berkeley conference in honor of Jerzy Neyman and Jack Kiefer. Wadsworth, pp 811–825

  • Basawa I (1991) Generalized score tests for composite hypotheses. In: Estimating functions, chap 8. Oxford Science Publications, Oxford, pp 131–131

  • Borgatti S, Foster P (2003) The network paradigm in organizational research: a review and typology. J Manage 29: 991–1013

    Google Scholar 

  • Brass D, Galaskiewicz J, Greve H, Tsai W (2004) Taking stock of networks and organizations: a multilevel perspective. Acad Manage J 47: 795–817

    Article  Google Scholar 

  • Burk W, Steglich C, Snijders T (2007) Beyond dyadic interdependence: actor-oriented models for co-evolving social networks and individual behaviors. Int J Behav Dev 31: 397–404

    Article  Google Scholar 

  • Campbell R, Starkey F, Holliday J, Audrey S, Bloor M, Parry-Langdon N, Hughes R, Moore L (2008) An informal school-based peer-led intervention for smoking prevention in adolescence (ASSIST): A cluster randomised trial. Lancet 371(9624): 1595–1602

    Article  Google Scholar 

  • Cox D (2006) Principles of statistical inference. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Cox D, Hinkley D (1974) Theoretical statistics. Chapman and Hall, London

    MATH  Google Scholar 

  • Doreian, P, Stokman, F (eds) (1996) Evolution of social networks. Gordon and Breach Publishers, Newark

    Google Scholar 

  • Doreian, P, Stokman, F (eds) (2001) Evolution of social networks. Gordon and Breach Publishers, Newark

    Google Scholar 

  • Doreian, P, Stokman, F (eds) (2003) Evolution of social networks. Gordon and Breach Publishers, Newark

    Google Scholar 

  • Fawcett T (2006) An introduction to roc analysis. Pattern Recognit Lett 27: 861–874

    Article  Google Scholar 

  • Greene W (2007) Econometric analysis, 6th edn. Prentice Hall, New Jersey

    Google Scholar 

  • Handcock M (2003) Assessing degeneracy in statistical models of social networks. Center for Statistics and the Social Sciences, University of Washington. Available from: http://www.csss.washington.edu/Papers

  • Lee L (1982) Specification error in multinomial logit models. J Econom 20: 197–209

    Article  MATH  Google Scholar 

  • Lehmann EL, Romano JP (2005) Testing statistical hypotheses, 3rd edn. Springer, New York

    MATH  Google Scholar 

  • Maddala G (1983) Limited-dependent and qualitative variables in econometrics, 3rd edn. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • McFadden D (1973) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142

    Google Scholar 

  • Neyman J (1959) Optimal asymptotic tests of composite statistical hypotheses. In: Grenander U (ed) Probability and statistics. The Harald Cramér Volume. Wiley, New York, pp 213–234

    Google Scholar 

  • Norris J (1997) Markov chains. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Pearson M, Michell L (2000) Smoke rings: social network analysis of friendship groups, smoking, and drug-taking. Drugs Educ Prev Policy 7: 21–37

    Article  Google Scholar 

  • Rao C (1948) Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Proc Cambridge Philos Soc 44: 50–57

    Article  MathSciNet  MATH  Google Scholar 

  • Rao C, Poti S (1946) On locally most powerful tests when alternatives are one sided. Indian J Stat 7(4): 439

    MATH  Google Scholar 

  • Ripley R, Snijders T (2009) Manual for RSiena version 4.0

  • Rippon P, Rayner JCW (2010) Generalised score and Wald tests. Adv Decis Sci 2010: 8

    Google Scholar 

  • Ruud P (1983) Sufficient conditions for the consistency of maximum likelihood estimation despite misspecification of distribution in multinomial discrete models. Econometrica 51: 225–228

    Article  MathSciNet  MATH  Google Scholar 

  • Schweinberger M (2007) Chapter 4: satistical modeling of network panel data: goodness of fit. PhD thesis, University of Groningen, Groningen

  • Schweinberger M, Snijders T (2007) Markov models for digraph panel data: Monte carlo-based derivative estimation. Comput Stat Data Anal 51(9): 4465–4483

    Article  MathSciNet  MATH  Google Scholar 

  • Snijders T (2001) The statistical evaluation of social network dynamics. In: Sobel M, Becker M (eds) Sociological methodology. Blackwell, London, pp 361–395

    Google Scholar 

  • Snijders T (2002) Markov chain Monte Carlo estimation of exponential random graph models. J Soc Struct 3: 1–40

    Google Scholar 

  • Snijders T (2005) Models for longitudinal network data. In: Carrington P, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, Cambridge, pp 215–247

    Google Scholar 

  • Snijders T (2009) Longitudinal methods of network analysis. In: Meyers B (ed) Encyclopedia of complexity and system science. Springer, Berlin, pp 5998–6013

    Google Scholar 

  • Snijders T, Steglich C, Schweinberger M (2007) Modeling the co-evolution of networks and behavior. In: Montfort K, Oud H, Satorra A (eds) Longitudinal models in the behavioral and related sciences. Lawrence Erlbaum, Hillsdale, pp 41–71

    Google Scholar 

  • Snijders T, van de Bunt C, Steglich C (2010a) Introduction to actor-based models for network dynamics. Soc Netw 32: 44–60

    Article  Google Scholar 

  • Snijders TA, Koskinen J, Schweinberger M (2010b) Maximum likelihood estimation for social network dynamics. Ann Appl Stat 4: 567–588

    Article  MATH  Google Scholar 

  • Steglich C, Sinclair P, Holliday J, Moore L (2010) Actor-based analysis of peer influence in a stop smoking in schools trial (ASSIST). Soc Netw (in press)

  • Strauss D (1986) On a general class of models for interaction. SIAM Rev 28: 513–527

    Article  MathSciNet  MATH  Google Scholar 

  • Van De Bunt GG, Van Duijn MAJ, Snijders T (1999) Friendship networks through time: An actor-oriented dynamic statistical network model. Comput Math Organ Theory 5(2): 167–192

    Article  MATH  Google Scholar 

  • Wooldridge J (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge

    MATH  Google Scholar 

  • Yatchew A, Griliches Z (1985) Specification error in probit models. Rev Econ Stat 67: 134–139

    Article  Google Scholar 

  • Zweig M, Campbell G (1993) Receiver-operating characteristic (roc) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(8): 561–577

    Google Scholar 

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Correspondence to Joshua A. Lospinoso.

Additional information

This research was funded in part by U.S. Army Project Number 611102B74F and MIPR Number 9FDATXR048 (JAL); by U.S. N.I.H. (National Institutes of Health) Grant Number 1R01HD052887-01A2, for the project Adolescent Peer Social Network Dynamics and Problem Behavior (TABS and RMR); and by U.S. N.I.H. (National Institutes of Health) Grant Number 1R01GM083603-01 (MS).

The authors are grateful to Professor Laurence Moore of the Cardiff Institute for Society, Health and Ethics (CISHE) for the permission to use the ASSIST data and to Christian Steglich for his help with using these data.

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Lospinoso, J.A., Schweinberger, M., Snijders, T.A.B. et al. Assessing and accounting for time heterogeneity in stochastic actor oriented models. Adv Data Anal Classif 5, 147–176 (2011). https://doi.org/10.1007/s11634-010-0076-1

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Mathematics Subject Classification (2000)

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