Statistical Power in Longitudinal Network Studies

Christoph Stadtfeld, Tom A. B. Snijders, Christian Steglich and Marijtje van Duijn

Sociological Methods & Research, 2020, vol. 49, issue 4, 1103-1132

Abstract: 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.

Keywords: statistical power; selection and influence; missing data; research design; stochastic actor-oriented models; SIENA; network simulation; social network analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:49:y:2020:i:4:p:1103-1132

DOI: 10.1177/0049124118769113

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