# Statistical Power in Longitudinal Network Studies

## TL;DR

This paper introduces a simulation-based method to assess the statistical power of longitudinal social network studies using stochastic actor-oriented models, emphasizing the importance of study design factors.

## Contribution

It provides a novel simulation procedure to evaluate power in longitudinal network studies, highlighting key factors affecting statistical validity.

## Key findings

- Statistical power is heavily influenced by network size and number of data waves.
- Missing data and participant turnover significantly reduce power.
- Design considerations are crucial for reliable longitudinal social network analysis.

## 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.