Statistical Power in Longitudinal Network Studies

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
224 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)1103-1132
Number of pages30
JournalSociological Methods & Research
Volume49
Issue number4
Early online date1-May-2018
DOIs
Publication statusPublished - 1-Nov-2020

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