Cargando…

Multilevel longitudinal analysis of social networks

Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testi...

Descripción completa

Detalles Bibliográficos
Autores principales: Koskinen, Johan, Snijders, Tom A B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376442/
https://www.ncbi.nlm.nih.gov/pubmed/37521824
http://dx.doi.org/10.1093/jrsssa/qnac009
_version_ 1785079272017231872
author Koskinen, Johan
Snijders, Tom A B
author_facet Koskinen, Johan
Snijders, Tom A B
author_sort Koskinen, Johan
collection PubMed
description Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover.
format Online
Article
Text
id pubmed-10376442
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-103764422023-07-29 Multilevel longitudinal analysis of social networks Koskinen, Johan Snijders, Tom A B J R Stat Soc Ser A Stat Soc Original Article Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover. Oxford University Press 2023-01-23 /pmc/articles/PMC10376442/ /pubmed/37521824 http://dx.doi.org/10.1093/jrsssa/qnac009 Text en © (RSS) Royal Statistical Society 2023. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Article
Koskinen, Johan
Snijders, Tom A B
Multilevel longitudinal analysis of social networks
title Multilevel longitudinal analysis of social networks
title_full Multilevel longitudinal analysis of social networks
title_fullStr Multilevel longitudinal analysis of social networks
title_full_unstemmed Multilevel longitudinal analysis of social networks
title_short Multilevel longitudinal analysis of social networks
title_sort multilevel longitudinal analysis of social networks
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10376442/
https://www.ncbi.nlm.nih.gov/pubmed/37521824
http://dx.doi.org/10.1093/jrsssa/qnac009
work_keys_str_mv AT koskinenjohan multilevellongitudinalanalysisofsocialnetworks
AT snijderstomab multilevellongitudinalanalysisofsocialnetworks