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Statistical Power in Longitudinal Network Studies
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DOI：10.1177/0049124118769113.png)
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Abstract
En 中文
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
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