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Statistical Power in Longitudinal Network Studies
ETH Zurich.
University of Groningen.
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-9097-0873
University of Groningen.
2018 (English)In: Sociological Methods & Research, ISSN 0049-1241, E-ISSN 1552-8294Article in journal (Refereed) Epub ahead of print
Abstract [en]

Longitudinal social network studies can easily suffer from insufficient statistical power. Studies that simultaneously investigate change of network ties and change of nodal attributes (selection and influence studies) are particularly at risk because the number of nodal observations is typically much lower than the number of observed tie variables. This article presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models are to be applied. Two detailed case studies illustrate how statistical power is strongly affected by network size, number of data collection waves, effect sizes, missing data, and participant turnover. These issues should thus be explored in the design phase of longitudinal social network studies.

Place, publisher, year, edition, pages
Sage Publications, 2018.
Keywords [en]
statistical power, selection and influence, missing data, research design, stochastic actor-oriented models, SIENA, network simulation, social network analysis
National Category
Sociology (excluding Social Work, Social Psychology and Social Anthropology)
Identifiers
URN: urn:nbn:se:liu:diva-147686DOI: 10.1177/0049124118769113OAI: oai:DiVA.org:liu-147686DiVA, id: diva2:1204029
Available from: 2018-05-05 Created: 2018-05-05 Last updated: 2018-05-09Bibliographically approved

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Steglich, Christian

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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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