Search results
Results: 94
Number of items: 94
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Haslbeck, J. M. B., Epskamp, S., Marsman, M., & Waldorp, L. J. (2021). Interpreting the Ising Model: The Input Matters. Multivariate Behavioral Research, 56(2), 303-313. https://doi.org/10.1080/00273171.2020.1730150 -
Epskamp, S., Fried, E. I., van Borkulo, C. D., Robinaugh, D. J., Marsman, M., Dalege, J., Rhemtulla, M., & Cramer, A. O. J. (2021). Investigating the Utility of Fixed-margin Sampling in Network Psychometrics. Multivariate Behavioral Research, 56(2), 314-328 . https://doi.org/10.1080/00273171.2018.1489771 -
Boehm, U., Marsman, M., van der Maas, H. L. J., & Maris, G. (2021). An Attention-Based Diffusion Model for Psychometric Analyses. Psychometrika, 86(4), 938-972. https://doi.org/10.1007/s11336-021-09783-0 -
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
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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
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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 -
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
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