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Statistical Power in Longitudinal Network Studies

Author

Listed:
  • Christoph Stadtfeld
  • Tom A. B. Snijders
  • Christian Steglich
  • Marijtje van Duijn

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 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.

Suggested Citation

  • Christoph Stadtfeld & Tom A. B. Snijders & Christian Steglich & Marijtje van Duijn, 2020. "Statistical Power in Longitudinal Network Studies," Sociological Methods & Research, , vol. 49(4), pages 1103-1132, November.
    • Handle: RePEc:sae:somere:v:49:y:2020:i:4:p:1103-1132
    DOI: 10.1177/0049124118769113
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    Cited by:

    1. Guéneau, Grégory & Chabaud, Didier & Sauvannet, Marie-Christine Chalus, 2023. "Sticky ties: Quest for structural inter-organizational configurations in entrepreneurial ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    2. Liu, Congzheng & Letchford, Adam N. & Svetunkov, Ivan, 2022. "Newsvendor problems: An integrated method for estimation and optimisation," European Journal of Operational Research, Elsevier, vol. 300(2), pages 590-601.
    3. Sun, Chuanwang & Khan, Anwar & Liu, Yongzhe & Lei, Ni, 2022. "An analysis of the impact of fiscal and monetary policy fluctuations on the disaggregated level renewable energy generation in the G7 countries," Renewable Energy, Elsevier, vol. 189(C), pages 1154-1165.
    4. Hongshu Chen & Xinna Song & Qianqian Jin & Ximeng Wang, 2022. "Network dynamics in university-industry collaboration: a collaboration-knowledge dual-layer network perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6637-6660, November.
    5. Chen, Jiandong & Xu, Chong & Shahbaz, Muhammad & Song, Malin, 2021. "Interaction determinants and projections of China’s energy consumption: 1997–2030," Applied Energy, Elsevier, vol. 283(C).
    6. Adamczyk, Paulina & Woźniak, Wojciech, 2021. "Prewencja zachowań samobójczych jako zadanie polityki zdrowia publicznego. Doświadczenia Finlandii, inspiracja dla Polski?," Studia z Polityki Publicznej / Public Policy Studies, Warsaw School of Economics, vol. 8(4), pages 1-24, December.
    7. Rodney P. Jones, 2021. "Were the hospital bed reductions proposed by English Clinical Commissioning Groups (CCGs) in the sustainability and transformation plans (STPs) achievable? Insights from a new model to compare interna," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(2), pages 459-481, March.
    8. Colin Gallagher & Dean Lusher & Johan Koskinen & Bopha Roden & Peng Wang & Aaron Gosling & Anastasios Polyzos & Martina Stenzel & Sarah Hegarty & Thomas Spurling & Gregory Simpson, 2023. "Network patterns of university-industry collaboration: A case study of the chemical sciences in Australia," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4559-4588, August.
    9. Ogawa, Keishi & Garrod, Guy & Yagi, Hironori, 2023. "Sustainability strategies and stakeholder management for upland farming," Land Use Policy, Elsevier, vol. 131(C).

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