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Results: 96
Number of items: 96
  • van den Bergh, D., van Doorn, J., Marsman, M., Draws, T., van Kesteren, E.-J., Derks, K., Dablander, F., Gronau, Q. F., Kucharský, Š., Komarlu Narendra Gupta, A. R., Sarafoglou, A., Voelkel, J. G., Stefan, A., Ly, A., Hinne, M., Matzke, D., & Wagenmakers, E.-J. (2020). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASP. Année Psychologique, 120(1), 73-96. https://doi.org/10.31234/osf.io/spreb, https://doi.org/10.3917/anpsy1.201.0073
  • 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
    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
  • Open Access
    Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., Kucharský, S., Derks, K., Gronau, Q. F., Raj, A., Boehm, U., van Kesteren, E.-J., Hinne, M., Matzke, D., Marsman, M., & Wagenmakers, E.-J. (2020). The Bayesian Methodology of Sir Harold Jeffreys as a Practical Alternative to the P Value Hypothesis Test. Computational Brain & Behavior, 3(2), 153-161. https://doi.org/10.31234/osf.io/dhb7x, https://doi.org/10.1007/s42113-019-00070-x
  • 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
    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
    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
    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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