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Submitted for Publication

Bechger, T. M., Maris, G. K. J., & Marsman, M. (2018). An asymptotically efficient sampler for bayesian inference in educational measurement. ArXiv, 1808.03947.

Boehm, U., MarsmanM., van der Maas, H. L. J., & Maris, G. K. J. (2019). A Simple Diffusion Model for Psychometric Analyses.

Haslbeck, J., Epskamp, S., Marsman, M., & Waldorp, L. J. (2018). Interpreting the Ising model: The input matters. 

Marsman, M. (2019). The idiographic Ising model. Retrieved from:

Marsman, M., Waldorp, L. J., & Borsboom, D. (2019). Towards a grand unified theory of network models: Reply to Brusco, Steinley, Hoffman, Davis-Stober, & Wasserman. Retrieved from:

van Bork, R., Marsman, M., Rhemtulla, M., Epskamp, S., Kruis, J., & Borsboom, D. (2018). Common effect models: Positive or negative manifold?

van Doorn, J. B., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2017). Bayesian latent-normal inference for the rank sum test, the signed rank test and Spearman’s rho.

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. (2018). The JASP Guidelines for Conducting and Reporting a Bayesian Analysis.

Waldorp, L.J., & Marsman, M. (2019). Science is about theory not method.

In press

Epskamp, S., Fried, E., van Borkulo, C., Robinaugh, D. J., Marsman, M, Dalege, J., Rhemtulla, M., & Cramer, A. (in press). Investigating the utility of fixed-margin sampling in network psychometrics. Multivariate Behavioral Research.

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. (in press). Crowdsourcing hypothesis tests: Making transparent how design choices shape research results. Psychological Bulletin.

Ly, A., Etz, A., MarsmanM., & Wagenmakers, E.-J. (in press). Replication Bayes factors from evidence updatingBehavior Research Methods.

van der Maas, H. L. J., Savi, A. O., Hofman, A., Kan, K.-J., & Marsman, M. (in press). The network approach to general intelligence. In D. J. McFarland (Ed.), General and specific mental abilities (pp. 108–131). Cambridge Scholars Publishing.


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

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 Veldkamp, B. P., & Sluijter, C. (Eds.), Theoretical and Practical Advances in Computer-Based Educational Measurement (pp. 93-120). Springer Nature Switzerland AG. Doi: 10.1007/978-3-030-18480-3_5

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

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 estimation of Kendall’s Tau using a latent normal approach. Statistics and Probability Letters, 145, 268-272.

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 lifespan. Developmental Psychology, 53(2), 379-395. [doi]

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. [doi]

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.


Marsman, M., Maris, G. K. J., Bechger, T. M., & Glas, C. A. W. (2011). A Conditional Composition Algorithm for Latent Regression (Measurement and Research Department Reports No. 11–02). Arnhem, the Netherlands: Cito.