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

• Bayesian Graphical Modeling Lab

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.