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Results: 408
Number of items: 408
  • Forscher, P. S., Wagenmakers, E.-J., Coles, N. A., Silan, M. A., Dutra, N., Basnight-Brown, D., & IJzerman, H. (2023). The Benefits, Barriers, and Risks of Big-Team Science. Perspectives on Psychological Science, 18(3), 607-623. https://doi.org/10.1177/17456916221082970
  • Open Access
    Hardwicke, T. E., & Wagenmakers, E.-J. (2023). Reducing bias, increasing transparency and calibrating confidence with preregistration. Nature Human Behaviour, 7, 15-26. https://doi.org/10.1038/s41562-022-01497-2
  • Open Access
    Dablander, F. (2023). Changing systems: Statistical, causal, and dynamical perspectives. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    van den Bergh, D., Wagenmakers, E.-J., & Aust, F. (2023). Bayesian Repeated-Measures Analysis of Variance: An Updated Methodology Implemented in JASP. Advances in Methods and Practices in Psychological Science, 6(2). https://doi.org/10.1177/25152459231168024
  • Open Access
    Bartoš, F., & Wagenmakers, E.-J. (2023). A general approximation to nested Bayes factors with informed priors. Stat, 12(1), Article e600. https://doi.org/10.1002/sta4.600
  • Open Access
    Sarafoglou, A. (2023). Good research practices. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Hoogeveen, S. (2023). To believe or not to believe: Open science and replication in the psychology of religion. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Wagenmakers, E.-J., & Ly, A. (2023). History and nature of the Jeffreys–Lindley paradox. Archive for History of Exact Sciences, 77(1), 25-72. https://doi.org/10.1007/s00407-022-00298-3
  • Open Access
    Pawel, S., Aust, F., Held, L., & Wagenmakers, E.-J. (2023). Normalized power priors always discount historical data. Stat, 12(1), Article e591. https://doi.org/10.1002/sta4.591
  • Open Access
    van Doorn, J., Haaf, J. M., Stefan, A. M., Wagenmakers, E.-J., Cox, G. E., Davis-Stober, C. P., Heathcote, A., Heck, D. W., Kalish, M., Kellen, D., Matzke, D., Morey, R. D., Nicenboim, B., van Ravenzwaaij, D., Rouder, J. N., Schad, D. J., Shiffrin, R. M., Singmann, H., Vasishth, S., ... Aust, F. (2023). Bayes Factors for Mixed Models: a Discussion. Computational Brain and Behavior, 6(1), 140–158. https://doi.org/10.1007/s42113-022-00160-3
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