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Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses

Author

Listed:
  • Dino Dittrich
  • Roger Th. A. J. Leenders
  • Joris Mulder

Abstract

Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This article presents flexible Bayes factor testing procedures that do not have these limitations. We propose Bayes factors based on an empirical and a uniform prior for the network effect, respectively, first. Next, we develop a fractional Bayes factor where a default prior is automatically constructed. Simulation results suggest that the first two Bayes factors show superior performance and are the Bayes factors we recommend. We apply the recommended Bayes factors to three data sets from the literature and compare the results to those coming from classical analyses using p values. R code for efficient computation of the Bayes factors is provided.

Suggested Citation

  • Dino Dittrich & Roger Th. A. J. Leenders & Joris Mulder, 2019. "Network Autocorrelation Modeling: A Bayes Factor Approach for Testing (Multiple) Precise and Interval Hypotheses," Sociological Methods & Research, , vol. 48(3), pages 642-676, August.
    • Handle: RePEc:sae:somere:v:48:y:2019:i:3:p:642-676
    DOI: 10.1177/0049124117729712
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    References listed on IDEAS

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    Cited by:

    1. Jina Park & Ick Hoon Jin & Minjeong Jeon, 2023. "How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1529-1555, December.
    2. Vennis Hong & Sage K Iwamoto & Rei Goto & Sean Young & Sukhawadee Chomduangthip & Natirath Weeranakin & Akihiro Nishi, 2020. "Socio-demographic determinants of motorcycle speeding in Maha Sarakham, Thailand," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-11, December.
    3. Orsoni, Matteo & Spinoso, Matilde & Garofalo, Sara & Mazzoni, Noemi & Giovagnoli, Sara & de Chiusole, Debora & Anselmi, Pasquale & Bacherini, Alice & Pierluigi, Irene & Stefanutti, Luca & Balboni, Giu, 2025. "Bayesian networks to evaluate and test the Raven’s colored progressive matrices," Intelligence, Elsevier, vol. 113(C).

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