IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v76y2022i2p219-235.html
   My bibliography  Save this article

Bayesian model selection for multilevel mediation models

Author

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/stan.12256
    Download Restriction: no
    File URL: https://libkey.io/10.1111/stan.12256?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    2. Russell B. Millar, 2009. "Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors," Biometrics, The International Biometric Society, vol. 65(3), pages 962-969, September.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
    4. Li, Yong & Yu, Jun, 2012. "Bayesian hypothesis testing in latent variable models," Journal of Econometrics, Elsevier, vol. 166(2), pages 237-246.
    5. Ferra Yanuar & Kamarulzaman Ibrahim & Abdul Aziz Jemain, 2013. "Bayesian structural equation modeling for the health index," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1254-1269, June.
    6. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    2. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    3. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    4. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
    5. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    7. Chen, Liyuan & Zerilli, Paola & Baum, Christopher F., 2019. "Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications," Energy Economics, Elsevier, vol. 79(C), pages 111-129.
    8. Arnab Kumar Maity & Sanjib Basu & Santu Ghosh, 2021. "Bayesian criterion‐based variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 835-857, August.
    9. Bresson Georges & Chaturvedi Anoop & Rahman Mohammad Arshad & Shalabh, 2021. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 75-97, May.
    10. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    11. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    12. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2018. "Modeling volatility dynamics using non-Gaussian stochastic volatility model based on band matrix routine," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 193-201.
    13. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    14. Gill Rowlands & David Whitney & Graham Moon, 2018. "Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems," IJERPH, MDPI, vol. 15(8), pages 1-8, August.
    15. Tien Thanh Thach & Radim Bris, 2020. "Improved new modified Weibull distribution: A Bayes study using Hamiltonian Monte Carlo simulation," Journal of Risk and Reliability, , vol. 234(3), pages 496-511, June.
    16. Fernanda B. Rizzato & Roseli A. Leandro & Clarice G.B. Demétrio & Geert Molenberghs, 2016. "A Bayesian approach to analyse overdispersed longitudinal count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 2085-2109, August.
    17. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    18. Diegel, Max, 2022. "Time-varying credibility, anchoring and the Fed's inflation target," Discussion Papers 2022/9, Free University Berlin, School of Business & Economics.
    19. Yong Li & Xiaobin Liu & Jun Yu & Tao Zeng, 2018. "A New Wald Test for Hypothesis Testing Based on MCMC outputs," Papers 1801.00973, arXiv.org.
    20. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:stanee:v:76:y:2022:i:2:p:219-235. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.