Predictors and outcomes of social network compositions. A compositional structural equation modeling approach
dc.contributor.author
dc.date.accessioned
2023-04-24T08:10:47Z
dc.date.available
2023-04-24T08:10:47Z
dc.date.issued
2013-01
dc.identifier.issn
0378-8733
dc.identifier.uri
dc.description.abstract
Proportions of a total, including social network compositions (proportions of partner, family, friends, etc.) lie in a restricted space, which challenges statistical analysis. Network compositions can be both dependent and explanatory variables and are usually measured with error by survey instruments. Structural equation models make it possible to correct measurement error bias. Coenders et al. (2011) fitted a factor analysis model to transformed network compositions. In this article, we use another transformation called an isometric log-ratio and we extend the model to include predictors and outcomes. The findings and hypotheses in the literature can be reformulated with isometric log-ratios in a more interpretable manner. For instance, we find relationships of gender with partner support, of education and extraversion with friend support, and of family support with tie multiplexity and closeness
dc.format.extent
10 p.
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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Verió postprint del document publicat a: https://doi.org/10.1016/j.socnet.2012.10.002
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© Social Networks, 2013, vol. 35, núm. 1, p. 1-10
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Articles publicats (D-EC)
dc.rights
Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
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dc.source
Kogovsek, Tina Coenders, Germà Hlebec, Valentina 2013 Predictors and outcomes of social network compositions. A compositional structural equation modeling approach Social Networks 35 1 1 10
dc.subject
dc.title
Predictors and outcomes of social network compositions. A compositional structural equation modeling approach
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.identifier.idgrec
018097
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1879-2111