The effects of omitting components in a multilevel model with social network effects

Suesse, T., Steel, D. and Tranmer, M. (2023) The effects of omitting components in a multilevel model with social network effects. Sociological Methods and Research, (doi: 10.1177/00491241231156972) (Early Online Publication)

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Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associated with each level, and accurate standard errors. Social network analysis is another important approach to analysing complex data that incoproate the social relationships between a number of individuals. Extended linear regression models, such as network autoregressive models, have been proposed that include the social network information to account for the dependencies between persons. In this article, we propose three types of models that account for both the multilevel structure and the social network structure together, leading to network autoregressive multilevel models. We investigate theoretically and empirically, using simulated data and a data set from the Dutch Social Behavior study, the effect of omitting the levels and the social network on the estimates of the regression coefficients, variance components, network autocorrelation parameter, and standard errors.

Item Type:Articles
Status:Early Online Publication
Glasgow Author(s) Enlighten ID:Tranmer, Professor Mark
Authors: Suesse, T., Steel, D., and Tranmer, M.
College/School:College of Social Sciences > School of Social and Political Sciences > Sociology Anthropology and Applied Social Sciences
Journal Name:Sociological Methods and Research
Publisher:SAGE Publications
ISSN (Online):1552-8294
Published Online:15 March 2023
Copyright Holders:Copyright © The Author(s) 2023
First Published:First published in Sociological Methods and Research 2023
Publisher Policy:Reproduced under a Creative Commons license

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