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Bayesian analysis of experimental and observational data: a review and illustration of the BANOVA R package

Author

Listed:
  • Michel Wedel

    (University of Maryland)

  • Chen Dong

    (Snapchat)

  • Anna Kopyakova

    (Objective Platform)

Abstract

This article provides a review of the BANOVA R package and an illustration of its uses in Marketing Analytics. The package allows users to conduct regression analyses and analysis of variance for between-subjects, within-subjects, and mixed designs, where the dependent variable follows one of a variety of continuous or discrete distribution functions and the data may have a hierarchical structure. The package uses stan as the underlying computing engine, and enables the calculation of simple effects, floodlight analysis, and mediation analysis. The R package is illustrated through a reanalysis of the observational data by Blake et al. (Psychol Sci 32:315–325, 2021) on the relationship between misogynistic tweets and domestic violence, and of the experimental data by Srna et al. (Psychol Sci 29:1942–1955, 2018) on the perception of multitasking.

Suggested Citation

  • Michel Wedel & Chen Dong & Anna Kopyakova, 2024. "Bayesian analysis of experimental and observational data: a review and illustration of the BANOVA R package," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(3), pages 735-742, September.
    • Handle: RePEc:pal:jmarka:v:12:y:2024:i:3:d:10.1057_s41270-024-00312-3
    DOI: 10.1057/s41270-024-00312-3
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    3. Feihong Xia, 2023. "Why to use Poisson regression for count data analysis in consumer behavior research," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 379-384, September.
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