Statistical Power in Longitudinal Network Studies

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

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

30 Citaten (Scopus)
259 Downloads (Pure)

Samenvatting

Longitudinal social network studies may easily suffer from a lack of statistical power. This is the case in particular for studies that simultaneously investigate change of network ties and change of nodal attributes. Such selection and influence studies have become increasingly popular due to the introduction of stochastic actor-oriented models (SAOMs). This paper presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which SAOMs are to be applied. It describes how researchers can test different possible research designs decisions (e.g., about network delineation and study time) under uncertainty about the prevalence and strength of various social mechanisms. Two detailed case studies illustrate that network size, number of data collection waves, effect sizes, missing data, and participant turnover can have a serious effect on the statistical power of longitudinal social network studies.
Originele taal-2English
Pagina's (van-tot)1103-1132
Aantal pagina's30
TijdschriftSociological Methods & Research
Volume49
Nummer van het tijdschrift4
Vroegere onlinedatum1-mei-2018
DOI's
StatusPublished - 1-nov.-2020

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