Please go to the published source for citations and quotes. A direct link to the published source is provided when the work is open access.

Submitted for publication

Bosma, M. J., Vermeulen, J. M., Huth, K. B. S., de Haan, L., Alizadeh, B. Z., Simons, C. J. C., Marsman, M., & Schirmbeck, F. (2024). Exploring the Interactions between Psychotic Symptoms, Cognition, and Environmental Risk Factors: A Bayesian Analysis of Networks.

Huth, K. B. S., Zavlis, O., Luigjes, J., Galenkamp, H., Lok, A., Bockting, C., Goudriaan, A. E., Marsman, M., & van Holst, R. J. (2022). A Network Perspective on Ethnic, Religious, and Socioeconomic Factors in Alcohol Use—the HELIUS Study. [PsyArXiv]

Maier, M., Bartoš, F., Quintana, D. S., Dablander, F., van den Bergh, D., Marsman, M., Ly, A., Wagenmakers, E.-J. (2022). Model-Averaged Bayesian t-Tests. [PsyArXiv]

Marsman, M., van den Bergh, D., & Haslbeck, J. M. B. (2023). Bayesian Analysis of the Ordinal Markov Random Field. [PsyArXiv] [CRAN]

Sekulovski, N., Keetelaar, S., Haslbeck, J. M. B., & Marsman, M. (2023). Sensitivity Analysis of Prior Distributions in Bayesian Graphical Modeling: Guiding Informed Prior Choices for Conditional Independence Testing. [PsyArXiv]

Sekulovski, N., Marsman, M., & Wagenmakers, E.-J. (2024). A Good Check on the Bayes Factor. [PsyArXiv]

van Bork, R., Marsman, M., Rhemtulla, M., Epskamp, S., Kruis, J., & Borsboom, D. (2018). Common Effect Models: Positive or Negative Manifold? [PsyArXiv]

van der Pal, Z., Douw, L., Genis, A., van den Bergh, D., Marsman, M., Schrantee, A., & Blanken, T. (2024). Tell me why? A scoping review on the fundamental building blocks of fMRI networks. [PsyArXiv]

Waldorp, L. J., & Marsman, M. (2024). Evolving Networks, Markov Chains and Dynamical Systems.

Zavlis, O., Huth, K. B. S., Luigjes, J., Galenkamp, H., Lok, A., Stronks, K., Bockting, C. L. H., Goudriaan, A., Marsman, M., & van Holst, R. J. (2024). The interplay of alcohol use symptoms and sociodemographic factors in the Netherlands: A network perspective.

Accepted for publication

Hoogeveen, S., Borsboom, D., Kucharsky, S., Marsman, M., Molenaar D., de Ron, J., Sekulovski, N., Visser, I., van Elk, M., & Wagenmakers, E.-J. (in press). Prevalence, Patterns, and Predictors of Paranormal Beliefs in the Netherlands: A Several-Analysts Approach. Royal Society Open Science. [PsyArXiv]

Keetelaar, S., Sekulovski, N., Borsboom, D., & Marsman, M. (in press). Comparing Maximum Likelihood and Pseudo-Maximum Likelihood Estimators for the Ising Model. Advances .in/psychology. [PsyArXiv]

Sekulovski, N., Keetelaar, S., Wagenmakers, E.-J., van Bork, R., van den Bergh, D., & Marsman, M. (in press). Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods. Multivariate Behavioral Research.[PsyArXiv]


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, e66366. [PsyArXiv]


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 [PsyArXiv]

Marsman, M., & Huth, K. B. S. (2023). Idiographic Ising and Divide and Color Models: Encompassing networks for heterogeneous binary data. Multivariate Behavioral Research, 58, 787-814. [PsyArXiv].

Marsman, M., Waldorp, L. J., & Borsboom, D. (2023). Towards an Encompassing Theory of Network Models: Reply to Brusco, Steinley, Hoffman, Davis-Stober, & Wasserman. Psychological Methods, 28(4), 757-764. [PsyArXiv].

Sarafoglou, A., Aust, F., Marsman, M., Wagenmakers, E.-J., & Haaf, J. (2023). multibridge: An R Package to Evaluate Informed Hypotheses in Binomial and Multinomial Models. Behavior Research Methods, 55, 4343-4368. [PsyArXiv] [CRAN]

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, 322-338. [PsyArXiv] [CRAN]


Dalege, J., Haslbeck, J. M. B., & Marsman, M. (2022). Idealized modeling of psychological dynamics. In Isvorany, A.-M., Epskamp, S., Waldorp, L., & Borsboom D. (Eds.), Network Psychometrics with R (pp. 233-245).

Huth, K. B. S., Waldorp, L. J., Luigjes, J., Goudriaan, A. E., van Holst, R. J., & MarsmanM. (2022). A Note on the Structural Change Test in Finite Samples: Using a Permutation Approach to Estimate the Sampling Distribution. Psychometrika, 87, 1064-2080. [PsyArXiv]

Marsman, M., Bechger, T. M., & Maris, G. K. J. (2022). Composition Algorithms for Conditional Distributions. In van der Ark, L. A., Emons, W. H. M., & Meijer, R. R. (Eds.), Essays on Contemporary Psychometrics (pp. 219-250). Springer [PsyArXiv]

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.

MarsmanM., 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.

Mulder, J., Wagenmakers, E.-J., & MarsmanM. (2022). A Generalization of the Savage-Dickey Density Ratio for Testing Equality and Order Constrained HypothesesThe American Statistician, 76(2), 102-109.

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. [ArXiv]

Waldorp, L. J., & Marsman, M. (2022). Relations Between Networks, Regression, Partial Correlation, and Latent Variable ModelsMultivariate Behavioral Research, 57(6), 994-1006. [ArXiv]. Was awarded the Tanaka Award for best paper published in Multivariate Behavioral Research in 2022 by the Society for Multivariate Experimental Psychology.


Bechger, T. M., Maris, G. K. J., & Marsman, M. (2021). Bayesian Inference in Large-Scale Computational Psychometrics. In A. von Davier, R. J. Mislevy, & J. Hao (Eds.), Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment (pp. 109-131). Springer Nature Switzerland AG.

Boehm, U., MarsmanM., van der Maas, H. L. J., & Maris, G. K. J. (2021). An Attention-Based Diffusion Model for Psychometric Analyses. Psychometrika, 86(4), 938-972.

Haslbeck, J., Epskamp, S., Marsman, M., & Waldorp, L. J. (2021). Interpreting the Ising Model: The Input Matters. Multivariate Behavioral Research, 56(2), 303-313.

Huth, K. B. S., Luigjes, K., MarsmanM., Goudriaan, A. E., & van Holst, R. J. (2021). Modeling Alcohol Use Disorder As a Set of Interconnected Symptoms –Assessing Differences Between Clinical and Population Samples and Across External Factors. Addictive Behaviors, 125(107128).

Epskamp, S., Fried, E., van Borkulo, C., Robinaugh, D. J., Marsman, M, Dalege, J., Rhemtulla, M., & Cramer, A. (2021). Investigating the Utility of Fixed-Margin Sampling in Network Psychometrics. Multivariate Behavioral Research, 56(2), 314-328.

van den Bergh, D., Clyde, M. A., Raj, A., de Jong, T., Gronau, Q. F., Marsman, M., Ly, A., and Wagenmakers, E.-J. (2021). A Tutorial on Bayesian Multi-Model Linear Regression with BAS and JASP. Behavior Research Methods, 53, 2351-2371.

van Doorn, J., van den Bergh, D., Boehm, U., Dablander, F., Derks, K., Draws, T., Evans, N. J., Gronau, Q. F., Hinne, M., Kucharsky, S., 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, 813-826.

van Doorn, J., van den Bergh, D., Dablander, F., van Dongen, N., Derks, K., Evans, N., Gronau, Q. F., Haaf, J. M., Kunisato, Y., Ly, A., Marsman, M., Sarafoglou, A., Stefan, A., & Wagenmakers, E.-J. (2021). Strong Public Claims May Not Reflect Researchers’ Private Convictions. Significance, 18, 44-45.

Savi, O. A., MarsmanM., van der Maas, H. L. J. (2021). Evolving Networks of Human Intelligence. Intelligence, 88(101567).


Kruis, J., Maris, G. K. J., MarsmanM., 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(16226).

Landy, J.F., Jia, M. , 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., Christopher W., Bauman, C.W., Brady, W.J., Cheung, F., Cimpian, A., Dohle, S., Donnellan, M.B., Hahn, A., Hall, M.P., Jiménez-Leal, W., Johnson, D.J., Lucas, R.E., Monin, B., Montealegre, A., Mullen, E., Pang, J., Ray, J., Reinero, D.A., Reynolds, J., Sowden, W., Storage, D., Su, R., Tworek, C.M., van Bavel, J.J., Walco, D., Wills, J., Xu, X., Yam, K.C., Yang, X., Cunningham, W.A., Schweinsberg, M., Urwitz, M., the Crowdsourcing Hypothesis Tests Collaboration, & Uhlmann, E.L. (2020). Crowdsourcing Hypothesis Tests: Making Transparent How Design Choices Shape Research Results. Psychological Bulletin, 146(5), 451–479.

Ly, A., Stefan, A., van Doorn, J., Dablander, F., van den Bergh, D., Sarafoglou, A., Kucharský, Š., 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 TestComputational Brain & Behavior, 3, 153-161.

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. (2010). A Tutorial on Conducting and Interpreting a Bayesian ANOVA in JASPL’Année Psychologique/Topics in Cognitive Psychology, 120, 73-96. [press “télécharger” for the free pdf]

van Doorn, J. B., 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 Rho. Journal of Applied Statistics47(16), 2984–3006.


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: a Bayesian meta-analysis of individual participant dataNeuropsychology Review, 29(1), 52-78.

Borsboom, D. & Marsman, M. (2019). Latente variabelen en netwerken: Verschillende benaderingen van psychometrische data. Nieuw Archief voor Wiskunde, 20(3), 183–189.

Love, J., Selker, R., Marsman, M., Jamil, T., Dropmann, D., Verhagen, A. 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 designsJournal of Statistical Software88(2), 1–17. Doi: 10.18637/jss.v088.i02

Ly, A., Etz, A., MarsmanM., & Wagenmakers, E.-J. (2019). Replication Bayes factors from evidence updatingBehavior Research Methods, 51, 2498-2508.

Marsman, M., Sigurdardottir, H., Bolsinova, M., & Maris, G. K. J. (2019). Characterizing the manifest probability distributions of three latent trait models for accuracy and response timePsychometrika, 84(3), 870-891.

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). Springer Nature Switzerland AG.

Marsman, M., Waldorp, L. J., Dablander, F. & Wagenmakers, E.-J. (2019). Bayesian estimation of explained variance in ANOVA designs. Statistica Neerlandica73(3), 351-372.

Savi, O. A., Marsman, M., van der Maas, H. L. J., & Maris, G. K. J. (2019). The Wiring of IntelligencePerspectives on Psychological Science, 14(6), 1034-1061.

van der Maas, H. L. J., Savi, A. O., 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. [PsyArXiv]

van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2019). Bayesian estimation of Kendall’s tau using a latent normal approachStatistics & Probability Letters, 145, 268-272.

Waldorp, L. J., Marsman, M., & Maris, G. K. J. (2019). Logistic regression and Ising networks: Prediction and estimation when violating lasso assumptionsBehaviormetrika, 46(1), 49-72.


Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E.-J. (2018). On the importance of avoiding shortcuts in modelling hierarchical data. Behavior Research Methods, 50(4), 1614-1631.

Hofman, A., Visser, I., Jansen, B., Marsman, M. & van der Maas, H. L. J. (2018). Fast and slow strategies in multiplication. Learning and Individual Differences, 60, 30-40.

Ly, A., Marsman, M. & Wagenmakers, E.-J. (2018). Analytic posteriors for Pearson’s correlation coefficient. Statistica Neerlandica72(1), 4-13.

Ly, A., Raj, A., Marsman, M., Etz, A., & 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.

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 Research53(1), 15-35.

van Doorn, J. B., Ly, A., Marsman, M. & Wagenmakers, E.-J. (2018). Bayesian inference for Kendall’s rank correlation coefficient. The American Statistician72(4), 303-308.

Wagenmakers, E.-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, A. J., …, Morey, R. D. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58-76.

Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, A. J., Love, J., …, Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review25(1), 35-57.


Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PloS One, 12(6), e0179891.

Gronau, Q. F., Sarafoglou, A., Matzke, D., Boehm, U., Marsman, M., Leslie, D. S., Forster, J. J., Wagenmakers, E.-J., & Steingroever, H. (2017). A tutorial on Bridge Sampling. Journal of Mathematical Psychology, 81, 80-97.

Jamil, T., Ly., A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E.-J. (in press). Default “Gunel and Dickey” Bayes factors for contingency tablesBehavior Research Methods, 49(2), 638-652.

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.

Lever, A. G., Ridderinkhof, R., Marsman, M., & Geurts, H. M. (2017). Reactive and proactive interference control in adults with autism spectrum disorder across the lifespanDevelopmental Psychology, 53(2), 379-395.

Ly, A., Boehm, U., Heathcote, A., Turner, B. M., Forstmann, B., Marsman, M., & Matzke, D. (2017). 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). John Wiley & Sons.

Ly, A., Marsman, M., Verhagen, A. J., Grasman, R. P. P. P., & Wagenmakers, E.-J. (2017). A tutorial on Fisher Information. Journal of Mathematical Psychology, 80, 40-55.

Marsman, M., Maris, G. K. J., Bechger, T. M., & Glas, C. A. W. (2017). Turning simulation into estimation: Generalized exchange algorithms for exponential family models. PLoS One, 12(1), e0169787.

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(160426). Correction: We reanalysed the data from 14 studies published in a special issue for Social Psychology. Even though we referred to each of these 14 studies in our figures, we failed to include all them in the reference list. The omitted references can be found in the correction here.

Marsman, M. & Wagenmakers, E.-J. (2017). Bayesian benefits with JASP.  European Journal of Developmental Psychology14(5), 545-555.

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.

Marsman, M., Waldorp, L. J. & Maris, G. K. J. (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. 

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).


Marsman, M., Ly, A., Wagenmakers, E.-J. (2016). Four requirements for an acceptable research program.  Basic and Applied Social Psychology, 38(6), 308-312.

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.

Schweinsberg, M., Madan, N., Vianello, M., Sommer, S., Jordan, J., Tierney, W., … Uhlmann, E. (2016). The pipeline project: Pre-publication independent replications of a single laboratory’s research pipeline. Journal of Experimental Social Psychology, 66, 55-67.

Tierney, W., Schweinsberg, M., Jordan, J., Kennedy, D., Qureshi, I., Sommer, S., … Uhlmann, E. (2016). Data from a pre-publication independent replication initiative examining ten moral judgement effects. Scientific Data, 3(160082).


Love, J., Selker, R., Verhagen, J., Marsman, M., Gronau, Q. F., Jamil, T., …, Rouder, J. N. (2015). Software to sharpen your stats. Observer, 28.

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).


Fox, J.-P., Marsman, M., Mulder, J., & Verhagen, J. (2014). Complex latent variable modelling in educational assessment. Communications in Statistics — Simulation and Computation, 45(5), 1499-1510.

Marsman, M. (2014). Plausible Values in Statistical Inference (Unpublished doctoral thesis). University of Twente, Enschede, the Netherlands.


Marsman, M., Maris, G. K. J., & Bechger, T. M. (2012). Don’t tie yourself to an onion: Don’t tie yourself to assumptions of normality. In T. H. J. M. Eggen and B. P. Veldkamp (Eds.), Psychometrics in Practice at RCEC (pp. 85-94). Enschede, the Netherlands: RCEC.

Roelofs, E., van Onna, M., Brookhuis, K., Marsman, M., & de Penning, L. (2012). Designing Developmentally Tailored Driving Assessment Tasks for Formative Purposes. In L. Dorn (Ed.), Driver Behavior and Training volume 5 (pp. 61-80). Burlington, USA: Ashgate Publishing Company.