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Bayesian model selection for multilevel mediation models

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Listed:
  • Oludare Ariyo
  • Emmanuel Lesaffre
  • Geert Verbeke
  • Martijn Huisman
  • Martijn Heymans
  • Jos Twisk

Abstract

Mediation analysis is often used to explore the complex relationship between two variables through a third mediating variable. This paper aims to illustrate the performance of the deviance information criterion, the pseudo‐Bayes factor, and the Watanabe–Akaike information criterion in selecting the appropriate multilevel mediation model. Our focus will be on comparing the conditional criteria (given random effects) versus the marginal criteria (averaged over random effects) in this respect. Most of the previous work on the multilevel mediation models fails to report the poor behavior of the conditional criteria. We demonstrate here the superiority of the marginal version of the selection criteria over their conditional counterpart in the mediated longitudinal settings through simulation studies and via an application to data from the Longitudinal Aging Study of the Amsterdam study. In addition, we demonstrate the usefulness of our self‐written R function for multilevel mediation models.

Suggested Citation

  • Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Martijn Huisman & Martijn Heymans & Jos Twisk, 2022. "Bayesian model selection for multilevel mediation models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 219-235, May.
    • Handle: RePEc:bla:stanee:v:76:y:2022:i:2:p:219-235
    DOI: 10.1111/stan.12256
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    References listed on IDEAS

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    Cited by:

    1. Marko Sarstedt & Ovidiu-Ioan Moisescu, 2024. "Quantifying uncertainty in PLS-SEM-based mediation analyses," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(1), pages 87-96, March.

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