Time: Tuesday, 24/June/2025: 1:30pm - 4:30pm Session Chair: Johan Henrik Koskinen |
Stochastic Actor-oriented Models (SAOMs), as implemented in RSiena, are statistical models for analysing network dynamics. SAOMs assume that you have observed the network at at least two points in time. These models have been extended to handle many forms of longitudinal networks and could be said to collectively be regarded as the gold standard methods for such data.
Having observations on multiple networks, multilevel networks, is becoming increasingly common. This workshop deals with longitudinal analysis of such multilevel models, in particular the random coefficient multilevel longitudinal network analysis implemented in the function sienaBayes which is part of multiSiena, the sister package of RSiena. This method is based on the Stochastic Actor-oriented Model (SAOM). The basic idea of this random coefficient model will be presented, with the approach taken by the analysis using sienaBayes. The use of this function will be explained, and guidance will be given for parameter interpretation.
Topics treated are:
principles of Bayesian inference; the random coefficient multilevel version of the SAOM (ML-SAOM); MCMC estimation of the ML-SAOM; operation of sienaBayes; parameter interpretation.
Prerequisites
The workshop is intended for participants who know about the Stochastic Actor-oriented Model, and have practical experience in working with RSiena.
Literature:
Ripley, Ruth M., Tom A.B. Snijders, Zsofia Boda, Andras Voros, and Paulina Preciado (2023). Manual for RSiena.
URL: https://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf
Koskinen, Johan H. and Tom A.B. Snijders (2023). Multilevel longitudinal analysis of social networks. Journal of the Royal Statistical Society, Series A.
DOI: https://doi.org/10.1093/jrsssa/qnac009
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena
Maximum number of participants 30.
Introduction to the analysis of multilevel network dynamics using multiSiena
Stochastic Actor-oriented Models (SAOMs), as implemented in RSiena, are statistical models for analysing network dynamics. SAOMs assume that you have observed the network at at least two points in time. These models have been extended to handle many forms of longitudinal networks and could be said to collectively be regarded as the gold standard methods for such data.
Having observations on multiple networks, multilevel networks, is becoming increasingly common. This workshop deals with longitudinal analysis of such multilevel models, in particular the random coefficient multilevel longitudinal network analysis implemented in the function sienaBayes which is part of multiSiena, the sister package of RSiena. This method is based on the Stochastic Actor-oriented Model (SAOM). The basic idea of this random coefficient model will be presented, with the approach taken by the analysis using sienaBayes. The use of this function will be explained, and guidance will be given for parameter interpretation.
Topics treated are:
principles of Bayesian inference; the random coefficient multilevel version of the SAOM (ML-SAOM); MCMC estimation of the ML-SAOM; operation of sienaBayes; parameter interpretation.
Prerequisites
The workshop is intended for participants who know about the Stochastic Actor-oriented Model, and have practical experience in working with RSiena.
Literature:
Ripley, Ruth M., Tom A.B. Snijders, Zsofia Boda, Andras Voros, and Paulina Preciado (2023). Manual for RSiena.
URL: https://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf
Koskinen, Johan H. and Tom A.B. Snijders (2023). Multilevel longitudinal analysis of social networks. Journal of the Royal Statistical Society, Series A.
DOI: https://doi.org/10.1093/jrsssa/qnac009
SIENA website: http://www.stats.ox.ac.uk/~snijders/siena
Maximum number of participants 30.