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arXiv · 1701.05177

Statistical Power in Longitudinal Network Studies

Christoph Stadtfeld·Tom A. B. Snijders·Christian Steglich·Marijtje A. J. van Duijn
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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 paper presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models (SAOMs) 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.

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Christoph Stadtfeld, Tom A. B. Snijders, Christian Steglich, Marijtje A. J. van Duijn. 2018-03-04. Statistical Power in Longitudinal Network Studies. https://doi.org/10.1177/0049124118769113

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