Dr. M. (Maarten) Marsman
Faculty of Social and Behavioural Sciences
Programme group Psychological Methods
Area of expertise: psychometric theory, Bayesian inference, computational methods
Visiting address
- Nieuwe Achtergracht 129
- Room number: 0.32
Postal address
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Postbus 15906
1001 NK Amsterdam
Contact details
Social media
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Profile
I am an assistant professor at the Psychological Methods Unit of the University of Amsterdam. I received my Ph.D. in psychometrics from the University of Twente in 2014, while working at the educational testing organization Cito. My work combines psychometric theory with Bayesian inference and computational methods, focusing on Bayesian analysis of psychological data in general and graphical network models in particular. Under the umbrella of the Bayesian Graphical Modeling Lab, my team and I work extensively on developing the Bayesian approach to graphical network modeling of discrete psychometric data. This work allows researchers to express uncertainty in estimates of network parameters and structure, and to compute statistical evidence for the inclusion or exclusion of individual edges. These are practical advantages of the Bayesian approach to network analysis that enable researchers to report their results with confidence. My research has been funded by the Dutch Research Council (NWO) and the European Research Council (ERC). In addition to my research, I have also contributed to the development of the open-source statistical software JASP, which incorporates frequentist and Bayesian methods for common statistical analyses, and to several R software packages.
Other links
Key Publications
- Huth, K. B. S., de Ron, J., Goudriaan, A. E., Luigjes, J., Mohammadi, R., van Holst, R. J., Wagenmakers, E.-J., & Marsman, M. (2023). Bayesian Analysis of Cross-Sectional Networks: A Tutorial in R and JASP. Advances in Methods and Practices in Psychological Science, 6(4), 1-18.
- Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., van der Maas, H. L. J. & Maris, G. K. J. (2018). An introduction to Network Psychometrics: Relating Ising network models to item response theory models. Multivariate Behavioral Research, 53(1), 15-35.
- Marsman, M., Maris, G. K. J., Bechger, T. M., & Glas, C. A. W. (2015). Bayesian inference for low-rank Ising networks. Scientific Reports, 5(9050).
- Marsman, M., Maris, G. K. J., Bechger, T. M., & Glas, C. A. W. (2016). What can we learn from Plausible Values? Psychometrika, 81(2), 274-289.
- Marsman, M., Huth, K. B. S., Waldorp, L. J., & Ntzoufras, I. (2022). Objective Bayesian Edge Screening and Structure Selection for Ising Networks. Psychometrika, 87(1), 47-82.
- Waldorp, L. J., & Marsman, M. (2022). Relations Between Networks, Regression, Partial Correlation, and Latent Variable Models. Multivariate Behavioral Research, 57(6), 994-1006.
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Publications
- refereed (71)
- academic (7)
- professional (1)
- conference contributions (4)
- recognitions (16)
- supervised theses (8)
- working papers / preprints (1)
- datasets (4)
2025
- Marsman, M., van den Bergh, D., & Haslbeck, J. M. B. (2025). Bayesian Analysis of the Ordinal Markov Random Field. Psychometrika. Advance online publication. https://doi.org/10.1017/psy.2024.4
2024
- Bosma, M., Marsman, M., Vermeulen, J., Huth, K. B. S., de Haan, L., Alizadeh, B. Z., Simons, C. J. P., & Schirmbeck, F. (2024). Exploring the Interactions between Psychotic Symptoms, Cognition, and Environmental Risk Factors: A Bayesian Analysis of Networks. Schizophrenia Bulletin. Advance online publication. https://doi.org/10.1093/schbul/sbae174
Briganti, G., Scutari, M., Epskamp, S., Borsboom, D., Hoekstra, R. H. A., Fernandes Golino, H., Christensen, A. P., Morvan, Y., Ebrahimi, O. V., Costantini, G., Heeren, A., de Ron, J., Bringmann, L. F., Huth, K., Haslbeck, J. M. B., Isvoranu, A.-M., Marsman, M., Blanken, T., Gilbert, A., ... McNally, R. J. (2024). Network analysis: An overview for mental health research. International Journal of Methods in Psychiatric Research, 33(4), Article e2034. https://doi.org/10.1002/mpr.2034 [details]- Hoogeveen, S., Borsboom, D., Kucharský, Š., Marsman, M., Molenaar, D., de Ron, J., Sekulovski, N., Visser, I., van Elk, M., & Wagenmakers, E.-J. (2024). Prevalence, patterns and predictors of paranormal beliefs in The Netherlands: a several-analysts approach. Royal Society Open Science, 11(9), Article 240049. https://doi.org/10.1098/rsos.240049 [details]
- Huth, K. B. S., Keetelaar, S., Sekulovski, N., van den Bergh, D., & Marsman, M. (2024). Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package. Advances.in/psychology, 2, Article e66366. https://doi.org/10.56296/aip00010 [details]
- Keetelaar, S., Sekulovski, N., Borsboom, D., & Marsman, M. (2024). Comparing maximum likelihood and maximum pseudolikelihood estimators for the Ising model. Advances.in/psychology, 2, Article e25745. https://doi.org/10.56296/aip00013 [details]
- Maier, M., Bartoš, F., Quintana, D., Dablander, F., van den Bergh, D., Marsman, M., Ly, A., & Wagenmakers, E. M. (2024). Model-Averaged Bayesian t-Tests. Psychonomic Bulletin & Review. Advance online publication. https://doi.org/10.3758/s13423-024-02590-5
- Marsman, M., Sekulovski, N., Waldorp, L. J., & Haslbeck, J. M. B. (2024). A Bayesian Independent Samples T Test for Parameter Differences in Networks of Binary and Ordinal Variables. Manuscript submitted for publication. https://osf.io/preprints/osf/f4pk9
- Sekulovski, N., Blanken, T. F., Haslbeck, J. M. B., & Marsman, M. (2024). The Impact of Dichotomization on Network Recovery. Manuscript submitted for publication. https://osf.io/preprints/psyarxiv/93nxp
- Sekulovski, N., Keetelaar, S., Haslbeck, J., & Marsman, M. (2024). Sensitivity analysis of prior distributions in Bayesian graphical modeling: Guiding informed prior choices for conditional independence testing. Advances.in/psychology, 2, Article e92355. https://doi.org/10.56296/aip00016 [details]
- Sekulovski, N., Keetelaar, S., Huth, K., Wagenmakers, E.-J., van Bork, R., van den Bergh, D., & Marsman, M. (2024). Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods. Multivariate Behavioral Research, 59(5), 913-933. https://doi.org/10.1080/00273171.2024.2345915 [details]
- Sekulovski, N., Marsman, M., & Wagenmakers, E.-J. (2024). A Good check on the Bayes factor. Behavior Research Methods, 56(8), 8552–8566. https://doi.org/10.3758/s13428-024-02491-4 [details]
2023
- Huth, K. B. S., de Ron, J., Goudriaan, A. E., Luigjes, J., Mohammadi, R., van Holst, R. J., Wagenmakers, E.-J., & Marsman, M. (2023). Bayesian Analysis of Cross-Sectional Networks: A Tutorial in R and JASP. Advances in Methods and Practices in Psychological Science, 6(4), 1-18. https://doi.org/10.1177/25152459231193334 [details]
- Marsman, M., & Huth, K. (2023). Idiographic Ising and Divide and Color Models: Encompassing networks for heterogeneous binary data. Multivariate Behavioral Research, 58(4), 787-814. https://doi.org/10.1080/00273171.2022.2135089 [details]
- Marsman, M., Bechger, T. M., & Maris, G. K. J. (2023). Composition algorithms for conditional distributions. In L. A. van der Ark, W. H. M. Emons, & R. R. Meijer (Eds.), Essays on Contemporary Psychometrics (pp. 219-250). (Methodology of Educational Measurement and Assessment). Springer. https://doi.org/10.31234/osf.io/e5yjp, https://doi.org/10.1007/978-3-031-10370-4_12 [details]
- Sarafoglou, A., Aust, F., Marsman, M., Bartoš, F., Wagenmakers, E.-J., & Haaf, J. M. (2023). Multibridge: an R package to evaluate informed hypotheses in binomial and multinomial models. Behavior Research Methods, 55(8), 4343-4368. https://doi.org/10.3758/s13428-022-02020-1 [details]
- Sarafoglou, A., Haaf, J. M., Ly, A., Gronau, Q. F., Wagenmakers, E.-J., & Marsman, M. (2023). Evaluating Multinomial Order Restrictions With Bridge Sampling. Psychological Methods, 28(2), 322-338. https://doi.org/10.1037/met0000411 [details]
2022
- Dalege, J., Haslbeck, J. M. B., & Marsman, M. (2022). Idealized Modeling of Psychological Dynamics. In A.-M. Isvoranu, S. Epskamp, L. Waldrop, & D. Borsboom (Eds.), Network Psychometrics with R: A Guide for Behavioral and Social Scientists (pp. 233-245). (Research methods and statistics). Routledge. https://doi.org/10.4324/9781003111238-17 [details]
- Huth, K. B. S., Luigjes, J., Marsman, M., Goudriaan, A. E., & van Holst, R. J. (2022). Modeling alcohol use disorder as a set of interconnected symptoms -assessing differences between clinical and population samples and across external factors. Addictive Behaviors, 125, Article 107128. Advance online publication. https://doi.org/10.1016/j.addbeh.2021.107128 [details]
- Huth, K. B. S., Waldorp, L. J., Luigjes, J., Goudriaan, A. E., van Holst, R. J., & Marsman, M. (2022). A Note on the Structural Change Test in Highly Parameterized Psychometric Models. Psychometrika, 87(3), 1064-1080. Advance online publication. https://doi.org/10.1007/s11336-021-09834-6 [details]
- Marsman, M., & Rhemtulla, M. (2022). Guest Editors’ Introduction to The Special Issue “Network Psychometrics in Action”: Methodological Innovations Inspired by Empirical Problems. Psychometrika, 87(1), 1-11. https://doi.org/10.1007/s11336-022-09861-x [details]
- Marsman, M., Huth, K., Waldorp, L. J., & Ntzoufras, I. (2022). Objective Bayesian Edge Screening and Structure Selection for Ising Networks. Psychometrika, 87(1), 47-82. https://doi.org/10.1007/s11336-022-09848-8 [details]
- Mulder, J., Wagenmakers, E.-J., & Marsman, M. (2022). A Generalization of the Savage–Dickey Density Ratio for Testing Equality and Order Constrained Hypotheses. American Statistician, 76(2), 102-109. https://doi.org/10.1080/00031305.2020.1799861 [details]
- Sarafoglou, A., van der Heijden, A., Draws, T., Cornelisse, J., Wagenmakers, E.-J., & Marsman, M. (2022). Combine Statistical Thinking With Open Scientific Practice: A Protocol of a Bayesian Research Project. Psychology Learning and Teaching, 21(2), 138-150. https://doi.org/10.1177/14757257221077307 [details]
- Waldorp, L., & Marsman, M. (2022). Relations between Networks, Regression, Partial Correlation, and the Latent Variable Model. Multivariate Behavioral Research, 57(6), 994-1006. https://doi.org/10.1080/00273171.2021.1938959 [details]
2021
- 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 [details]
- 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 [details]
- 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. Advance online publication. https://doi.org/10.1080/00273171.2020.1730150 [details]
- Savi, A. O., Marsman, M., & van der Maas, H. L. J. (2021). Evolving networks of human intelligence. Intelligence, 88, Article 101567. https://doi.org/10.1016/j.intell.2021.101567 [details]
- van Doorn, J., van den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., Haaf, J. M., Hinne, M., Kucharský, Š., Ly, A., Marsman, M., Matzke, D., Komarlu Narendra Gupta, A. R., Sarafoglou, A., Stefan, A., Voelkel, J. G., & Wagenmakers, E-J. (2021). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 28(3), 813–826. Advance online publication. https://doi.org/10.3758/s13423-020-01798-5 [details]
- von den Bergh, D., Clyde, M. A., Gupta, A. R. K. N., de Jong, T., Gronau, Q. F., Marsman, M., Ly, A., & Wagenmakers, E.-J. (2021). A tutorial on Bayesian multi-model linear regression with BAS and JASP. Behavior Research Methods, 53(6), 2351-2371. https://doi.org/10.31234/osf.io/pqju6, https://doi.org/10.3758/s13428-021-01552-2 [details]
2020
- 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 [details]
- 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. Advance online publication. https://doi.org/10.1037/bul0000220 [details]
- 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. Advance online publication. https://doi.org/10.31234/osf.io/dhb7x, https://doi.org/10.1007/s42113-019-00070-x [details]
- 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 [details]
- 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 [details]
2019
- 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 [details]
- Love, J., Selker, R., Marsman, M., Jamil, T., Dropmann, D., Verhagen, J., Ly, A., Gronau, Q. F., Šmíra, M., Epskamp, S., Matzke, D., Wild, A., Knight, P., Rouder, J. N., Morey, R. D., & Wagenmakers, E-J. (2019). JASP: Graphical statistical software for common statistical designs. Journal of Statistical Software, 88, Article 2. https://doi.org/10.18637/jss.v088.i02 [details]
- Ly, A., Etz, A., Marsman, M., & Wagenmakers, E-J. (2019). Replication Bayes factors from evidence updating. Behavior Research Methods, 51(6), 2498–2508. Advance online publication. https://doi.org/10.3758/s13428-018-1092-x [details]
- Marsman, M., Sigurdardóttir, H., Bolsinova, M., & Maris, G. (2019). Characterizing the Manifest Probability Distributions of Three Latent Trait Models for Accuracy and Response Time. Psychometrika, 84(3), 870-891. https://doi.org/10.1007/s11336-019-09668-3 [details]
- 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 [details]
- 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 [details]
- Savi, A. O., Marsman, M., van der Maas, H. L. J., & Maris, G. K. J. (2019). The Wiring of Intelligence. Perspectives on Psychological Science, 14(6), 1034-1061. https://doi.org/10.1177/1745691619866447 [details]
- Waldorp, L., Marsman, M., & Maris, G. (2019). Logistic regression and Ising networks: Prediction and estimation when violating lasso assumptions. Behaviormetrika, 46(1), 49-72. Advance online publication. https://doi.org/10.1007/s41237-018-0061-0 [details]
- 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. Advance online publication. https://doi.org/10.1016/j.spl.2018.10.004 [details]
2018
- Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E-J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behavior Research Methods, 50(4), 1614-1631. https://doi.org/10.3758/s13428-018-1054-3 [details]
- Hofman, A. D., Visser, I., Jansen, B. R. J., Marsman, M., & van der Maas, H. L. J. (2018). Fast and slow strategies in multiplication. Learning and Individual Differences, 68, 30-40. https://doi.org/10.1016/j.lindif.2018.09.007 [details]
- Ly, A., Marsman, M., & Wagenmakers, E-J. (2018). Analytic posteriors for Pearson's correlation coefficient. Statistica Neerlandica, 72(1), 4-13. Advance online publication. https://doi.org/10.1111/stan.12111 [details]
- Ly, A., Raj, A., Etz, A., Marsman, M., Gronau, Q. F., & Wagenmakers, E-J. (2018). Bayesian reanalyses from summary statistics: A guide for academic consumers. Advances in Methods and Practices in Psychological Science, 1(3), 367-374. https://doi.org/10.1177/2515245918779348 [details]
- Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., van der Maas, H. L. J., & Maris, G. (2018). An introduction to network psychometrics: Relating ising network models to item response theory models. Multivariate Behavioral Research, 53(1), 15-35. https://doi.org/10.1080/00273171.2017.1379379 [details]
- Wagenmakers, E-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., Boutin, B., Meerhoff, F., Knight, P., Raj, A., van Kesteren, E-J., van Doorn, J., Šmíra, M., Epskamp, S., Etz, A., Matzke, D., ... Morey, R. D. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58-76. Advance online publication. https://doi.org/10.3758/s13423-017-1323-7 [details]
- Wagenmakers, E-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35-57. Advance online publication. https://doi.org/10.3758/s13423-017-1343-3 [details]
- van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E-J. (2018). Bayesian Inference for Kendall's Rank Correlation Coefficient. American Statistician, 72(4), 303-308. Advance online publication. https://doi.org/10.1080/00031305.2016.1264998 [details]
2017
- Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PLoS ONE, 12(6), Article e0179891. https://doi.org/10.1371/journal.pone.0179891 [details]
- Gronau, Q. F., Sarafoglou, A., Matzke, D., Ly, A., Boehm, U., Marsman, M., Leslie, D. S., Forster, J. J., Wagenmakers, E-M., & Steingroever, H. (2017). A tutorial on bridge sampling. Journal of Mathematical Psychology, 81, 80-97. Advance online publication. https://doi.org/10.1016/j.jmp.2017.09.005 [details]
- Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E-J. (2017). Default “Gunel and Dickey” Bayes factors for contingency tables. Behavior Research Methods, 49(2), 638-652. Advance online publication. https://doi.org/10.3758/s13428-016-0739-8 [details]
- Jamil, T., Marsman, M., Ly, A., Morey, R. D., & Wagenmakers, E-J. (2017). What Are the Odds? Modern Relevance and Bayes Factor Solutions for MacAlister’s Problem From the 1881 Educational Times. Educational and Psychological Measurement, 77(5), 819-830. Advance online publication. https://doi.org/10.1177/0013164416667980 [details]
- Lever, A. G., Ridderinkhof, K. R., Marsman, M., & Geurts, H. M. (2017). Reactive and Proactive Interference Control in Adults With Autism Spectrum Disorder Across the Lifespan. Developmental Psychology, 53(2), 379-395. Advance online publication. https://doi.org/10.1037/dev0000219 [details]
- Ly, A., Marsman, M., Verhagen, J., Grasman, R. P. P. P., & Wagenmakers, E-M. (2017). A tutorial on Fisher information. Journal of Mathematical Psychology, 80, 40-55. Advance online publication. https://doi.org/10.1016/j.jmp.2017.05.006 [details]
- Marsman, M., & Wagenmakers, E-J. (2017). Bayesian benefits with JASP. The European Journal of Developmental Psychology, 14(5), 545-555. Advance online publication. https://doi.org/10.1080/17405629.2016.1259614 [details]
- Marsman, M., & Wagenmakers, E-J. (2017). Three insights from a Bayesian interpretation of the one-sided P value. Educational and Psychological Measurement, 77(3), 529-539. Advance online publication. https://doi.org/10.1177/0013164416669201 [details]
- Marsman, M., Maris, G., Bechger, T., & Glas, C. (2017). Turning simulation into estimation: Generalized exchange algorithms for exponential family models. PLoS ONE, 12(1), Article e0169787. https://doi.org/10.1371/journal.pone.0169787 [details]
- Marsman, M., Schönbrodt, F. D., Morey, R. D., Yao, Y., Gelman, A., & Wagenmakers, E-J. (2017). A Bayesian bird's eye view of 'Replications of important results in social psychology'. Royal Society Open Science, 4(1), Article 160426. https://doi.org/10.1098/rsos.160426, https://doi.org/10.1098/rsos.170085 [details]
- Marsman, M., Waldorp, L., & Maris, G. (2017). A note on large-scale logistic prediction: Using an approximate graphical model to deal with collinearity and missing data. Behaviormetrika, 44(2), 513-534. https://doi.org/10.1007/s41237-017-0024-x [details]
- van der Maas, H. L. J., Kan, K.-J., Marsman, M., & Stevenson, C. E. (2017). Network Models for Cognitive Development and Intelligence. Journal of Intelligence, 5(2), Article 16. https://doi.org/10.3390/jintelligence5020016 [details]
2016
- Fox, J-P., Marsman, M., Mulder, J., & Verhagen, J. (2016). Complex Latent Variable Modeling in Educational Assessment. Communications in Statistics: Simulation and Computation, 45(5), 1499-1510. Advance online publication. https://doi.org/10.1080/03610918.2014.939518 [details]
- Marsman, M., Maris, G., Bechger, T., & Glas, C. (2016). What can we learn from Plausible Values? Psychometrika, 81(2), 274-289. https://doi.org/10.1007/s11336-016-9497-x [details]
- Schweinsberg, M., Madan, N., Vianello, M., Sommer, S. A., Jordan, J., Tierney, W., Awtrey, E., Zhu, L. L., Diermeier, D., Heinze, J. E., Srinivasan, M., Tannenbaum, D., Bivolaru, E., Dana, J., Davis-Stober, C. P., du Plessis, C., Gronau, Q. F., Hafenbrack, A. C., Liao, E. Y., ... Uhlmann, E. L. (2016). The pipeline project: Pre-publication independent replications of a single laboratory's research pipeline. Journal of Experimental Social Psychology, 66, 55-67. Advance online publication. https://doi.org/10.1016/j.jesp.2015.10.001 [details]
- Tierney, W., Schweinsberg, M., Jordan, J., Kennedy, D. M., Qureshi, I., Sommer, S. A., Thornley, N., Madan, N., Vianello, M., Awtrey, E., Zhu, L. L., Diermeier, D., Heinze, J. E., Srinivasan, M., Tannenbaum, D., Bivolaru, E., Dana, J., Davis-Stober, C. P., du Plessis, C., ... Uhlmann, E. L. (2016). Data from a pre-publication independent replication initiative examining ten moral judgement effects. Scientific Data, 3, Article 160082. https://doi.org/10.1038/sdata.2016.82 [details]
2015
- Love, J., Selker, R., Verhagen, J., Marsman, M., Gronau, Q. F., Jamil, T., Smira, M., Epskamp, S., Wild, A., Ly, A., Matzke, D., Wagenmakers, E-J., Morey, R. D., & Rouder, J. N. (2015). Software to sharpen your stats. APS Observer, 28(3), 27-29. http://www.psychologicalscience.org/index.php/publications/observer/2015/march-15/bayes-or-bust-with-new-softwares.html [details]
- Marsman, M., Maris, G., Bechger, T., & Glas, C. (2015). Bayesian inference for low-rank Ising networks. Scientific Reports, 5, Article 9050. https://doi.org/10.1038/srep09050 [details]
2023
- Marsman, M., Huth, K. B. S., Sekulovski, N., & van den Bergh, D. (2023). bgms: Bayesian analysis of networks of binary and/or ordinal variables. Software, CRAN. https://doi.org/10.32614/CRAN.package.bgms
- Marsman, M., Waldorp, L., & Borsboom, D. (2023). Towards an Encompassing Theory of Network Models: Reply to Brusco, Steinley, Hoffman, Davis-Stober, and Wasserman (2019). Psychological Methods, 28(4), 757-764. https://doi.org/10.1037/met0000373 [details]
2020
- Savi, A. O., van der Maas, H. L. J., Maris, G. K. J., & Marsman, M. (2020). Mitochondrial functioning ≠ general intelligence. Journal of Intelligence, 8(2), Article 20. https://doi.org/10.3390/jintelligence8020020 [details]
2019
- 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. [details]
2018
- Ly, A., Böhm, U., Heathcote, A., Turner, B. M., Forstmann, B., Marsman, M., & Matzke, D. (2018). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A. A. Moustafa (Ed.), Computational Models of Brain and Behavior (pp. 467-480). Wiley Blackwell. https://doi.org/10.1002/9781119159193.ch34 [details]
2017
- Wagenmakers, E. M., Marsman, M., van den Bergh, D., Chambers, C., Pashler, H., de Ruiter, J. P., Fischer, A., Giner-Sorolla, R., Inzlicht, M., Jonas, K. J., Cesario, J., Borsboom, D., van der Maas, H., Harris, C., Freitas, A., Vazire, S., Gervais, W., Milyavskaya, M., Dunn, E. W., ... Tullett, A. (2017). Suggestions to Advance Your Mission: An Open Letter to Dr. Shinobu Kitayama, Editor of JPSP:ASC. PsyArXiv. https://doi.org/10.31234/osf.io/39ugj
2016
- Marsman, M., Ly, A., & Wagenmakers, E-J. (2016). Four Requirements for an Acceptable Research Program. Basic and Applied Social Psychology, 38(6), 308-312. https://doi.org/10.1080/01973533.2016.1221349 [details]
2019
- 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 [details]
2017
- Ly, A., Raj, A., Wagenmakers, E. M., & Marsman, M. (2017). A limit-consistent Bayes factor for testing the equality of two Poisson rates. Poster session presented at International Workshop on Objective Bayes Methodology, Austin, United States.
2015
- Ly, A., Marsman, M., & Wagenmakers, E-J. (2015). Jeffrey's Correlation Test Based on Criteria of Bayarri, Berger, Forte, and Garcia-Donato (2012). Poster session presented at 11th International Workshop on Objective Bayes Methodology.
- Ly, A., Marsman, M., & Wagenmakers, E. M. (2015). Jeffreys’s Correlation Test Based on Criteria of Bayarri, Berger, Forte, and Garcia-Donato (2012). Poster session presented at Objective Bayesian Analysis, Valencia, Spain.
- Ly, A., Marsman, M., Epskamp, S., Matzke, D., Selker, R., Gronau, Q. F., Jamil, T., Steingroever, H., Love, J., & Wagenmakers, E-J. (2015). Replication Bayes Factors. Abstract from MathPsych 2015.
Prize / grant
- Marsman, M. (2024). NWO Innovational Research Incentives Scheme Vidi.
- Marsman, M. (2024). Early Career Award.
- Marsman, M. & Wagenmakers, E.-J. (2024). NWO Scientific Meetings and Consultations. https://www.nwo.nl/en/projects/21712
- Marsman, M. & Wagenmakers, E.-J. (2024). Workshop Grant.
- Waldorp, L. & Marsman, M. (2023). Tanaka Award for Best Article in Multivariate Behavioral Research. https://www.smep.org/awards/tanaka
- Marsman, M., Blanken, T. & Schrantee, A. (2022). ABC Project Grant.
- Marsman, M. (2021). ERC Starting Grant.
- Marsman, M. (2019). UMH Project Grant.
- Marsman, M. (2017). NWO Innovational Research Incentives Scheme Veni.
Membership / relevant position
- Marsman, M. (2018-2019). Institute for Advanced Studies. http://ias.uva.nl/
Journal editor
- Marsman, M. (editor) & Liu, Y. (editor) (2024-2026). British Journal of Mathematical & Statistical Psychology (Journal). https://bpspsychub.onlinelibrary.wiley.com/hub/journal/20448317/call-for-papers/si-2024-000810
- Marsman, M. (editor) & Rhemtulla, M. (editor) (2020-2022). Psychometrika (Journal).
- Marsman, M. (editor) (2019-2023). Psychometrika (Journal).
Talk / presentation
- Marsman, M. (speaker) (14-6-2018). The Idiographic Ising Network Model, 33rd IOPS Summer Conference, Amsterdam.
- van Beek, R. (speaker), Zerhouni, O. (speaker), Boffo, M. (speaker), Nikolaou, K. (speaker), Wiers, R. W. H. J. (speaker) & Marsman, M. (speaker) (1-9-2017). This but a scratch! Comparing frequentist and Bayesian meta-analysis on CBM on addictive behaviours., 31st annual conference of the European Health Psychology Society., Padua.
Others
- Godmann, H. (organiser), Marsman, M. (organiser), Wagenmakers, E.-J. (organiser), Alexander, M. (organiser), Friel, N. (organiser), Latouche, P. (organiser), Raftery, A. (organiser), Ryder, R. (organiser) & Véber, A. (organiser) (16-10-2024 - 18-10-2024). Bayesian Methods for the Social Sciences - II (organising a conference, workshop, ...). https://bayesforshs2.sciencesconf.org/
2024
2023
2022
2021
- Gronau, Q. F. (2021). Bayes factor model comparison for psychological science. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Kruis, J. (2021). Item response mechanics. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- van Doorn, J. B. (2021). Bayes factor hypothesis tests for ranks and researchers. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2018
- Kruis, J., Molenaar, D., Marsman, M., Maris, G., & van der Maas, H. (2018). The Pairwise Exchange Tournament. University of Amsterdam. https://doi.org/10.13140/RG.2.2.26027.54569 [details]
2023
- Epskamp, S., Fried, E. I., van Borkulo, C. D., Robinaugh, D. J., Marsman, M., Dalege, J., Rhemtulla, M. & Cramer, A. O. J. (2023). Investigating the Utility of Fixed-margin Sampling in Network Psychometrics. Taylor & Francis. https://doi.org/10.6084/m9.figshare.23301469.v1
2018
- Sarafoglou, A. S. G. & Marsman, M. (7-3-2018). Assignments Bayesian Statistics 2018. Universiteit van Amsterdam. https://doi.org/10.21942/uva.5955922.v1
- Sarafoglou, A. S. G. & Marsman, M. (6-3-2018). Final Grades BayesStats 2018. Universiteit van Amsterdam. https://doi.org/10.21942/uva.5952136.v1
2016
- van Doorn, J., Wagenmakers, E.-J., Eric-Jan, W., Ly, A. & Marsman, M. (2016). Bayesian Inference for Kendall's Rank Correlation Coefficient. Taylor & Francis. https://doi.org/10.6084/m9.figshare.4452449
This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list. -
Ancillary activities
- No ancillary activities
Briganti, G., Scutari, M., Epskamp, S., Borsboom, D., Hoekstra, R. H. A., Fernandes Golino, H., Christensen, A. P., Morvan, Y., Ebrahimi, O. V., Costantini, G., Heeren, A., de Ron, J., Bringmann, L. F., Huth, K., Haslbeck, J. M. B., Isvoranu, A.-M., Marsman, M., Blanken, T., Gilbert, A., ... McNally, R. J. (2024). Network analysis: An overview for mental health research. International Journal of Methods in Psychiatric Research, 33(4), Article e2034.