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Results: 95
Number of items: 95
  • Open Access
    Landy, J. F., Jia, M. L., Ding, I. L., Viganola, D., Tierney, W., Dreber, A., Johannesson, M., Pfeiffer, T., Ebersole, C. R., Gronau, Q. F., Ly, A., van den Bergh, D., Marsman, M., Derks, K., Wagenmakers, E.-J., Proctor, A., Bartels, D. M., Bauman, C. W., Brady, W. J., ... Uhlmann, E. L. (2020). Crowdsourcing hypothesis tests: Making transparent how design choices shape research results. Psychological Bulletin, 146(5), 451-479. https://doi.org/10.1037/bul0000220
  • Open Access
    van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2020). Bayesian rank-based hypothesis testing for the rank sum test, the signed rank test, and Spearman’s ρ. Journal of Applied Statistics, 47(16), 2984-3006. https://doi.org/10.1080/02664763.2019.1709053
  • Open Access
    Kruis, J., Maris, G., Marsman, M., Bolsinova, M., & van der Maas, H. L. J. (2020). Deviations of rational choice: an integrative explanation of the endowment and several context effects. Scientific Reports, 10, Article 16226. https://doi.org/10.1038/s41598-020-73181-2
  • van der Maas, H. L. J., Savi, O. A., Hofman, A., Kan, K.-J., & Marsman, M. (2019). The Network Approach to General Intelligence. In D. J. McFarland (Ed.), General and Specific Mental Abilities (pp. 108-131). Cambridge Scholars Publishing.
  • Open Access
    Waldorp, L., Marsman, M., & Maris, G. (2019). Logistic regression and Ising networks: Prediction and estimation when violating lasso assumptions. Behaviormetrika, 46(1), 49-72. https://doi.org/10.1007/s41237-018-0061-0
  • Open Access
    van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2019). Bayesian estimation of Kendall’s τ using a latent normal approach. Statistics and Probability Letters, 145, 268-272. https://doi.org/10.1016/j.spl.2018.10.004
  • Open Access
    Boffo, M., Zerhouni, O., Gronau, Q. F., van Beek, R. J. J., Nikolaou, K., Marsman, M., & Wiers, R. W. (2019). Cognitive Bias Modification for Behavior Change in Alcohol and Smoking Addiction: Bayesian Meta-Analysis of Individual Participant Data. Neuropsychology Review, 29(1), 52-78. https://doi.org/10.1007/s11065-018-9386-4
  • Open Access
    Marsman, M., Tanis, C. C., Bechger, T. M., & Waldorp, L. J. (2019). Network psychometrics in educational practice: Maximum likelihood estimation of the Curie-Weiss model. In B. P. Veldkamp, & C. Sluijter (Eds.), Theoretical and Practical Advances in Computer-Based Educational Measurement (pp. 93-120). (Methodology of Educational Measurement and Assessment). SpringerOpen. https://doi.org/10.1007/978-3-030-18480-3_5
  • Open Access
    Borsboom, D., & Marsman, M. (2019). Latente variabelen en netwerken: verschillende benaderingen van psychometrische data. Nieuw Archief voor Wiskunde, 20(3), 183-189. http://www.nieuwarchief.nl/serie5/toonnummer.php?deel=20&nummer=3&taal=0
  • Open Access
    Marsman, M., Waldorp, L., Dablander, F., & Wagenmakers, E.-J. (2019). Bayesian estimation of explained variance in ANOVA designs. Statistica Neerlandica, 73(3), 351-372. https://doi.org/10.1111/stan.12173
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