Maarten Marsman, University of Amsterdam

What network psychometrics can learn from classic psychometric and network modelling

Invited Speaker
Date & Time: Thursday, July 22 at 9:00AM EST


Network Psychometrics concerns itself with the network modelling of psychometric variables. We often borrow models and techniques for network modelling from distant fields, such as physics and machine learning. Even though these methods were a necessary tool to start afresh –distancing ourselves from the psychometric status quo– they were not designed for the challenges laid down by psychological data. In modelling psychological data, some pressing issues emerged in recent years. Population heterogeneity, expressing individual differences, and modelling (skewed) ordinal variables are concrete examples. Where traditional psychometric frameworks such as item response theory offer concrete modelling solutions to these challenges, network modelling is still far behind. 

Recent years saw a progressive accumulation of formal connections between Markov Random fields (i.e., network models) and traditional psychometric models –such as item response theory and factor models– and more recently, traditional network models –i.e., random graph models. Since classic psychometric and network modelling offers a wealth of solutions to acute modelling challenges, it is opportune to build on these formal connections to advance psychological network modelling. However, network psychometrics has not taken full advantage of what these modelling connections have to offer. 

In this talk, I dive into some concrete challenges in psychological network modelling and draft novel solutions that draw on the formal connections between Markov Random Fields and traditional modelling frameworks. 

About the Speaker

Maarten is an assistant professor at the Psychological Methods group of the University of Amsterdam, the Netherlands. His work revolves around network psychometrics and Bayesian statistics. In today’s talk, he will combine these research lines.

Maarten did a bachelor’s in psychology and a master’s in research methods at the University of Twente under the tutelage of Cees Glas. After his master’s, he worked on Bayesian statistical methods for large-scale educational surveys at Cito under the supervision of Gunter Maris, Timo Bechger, and Cees Glas. His dissertation comprised statistical theory surrounding plausible values and the statistical relations between psychometric models and networks.

Maarten continued working on Bayesian statistics and network psychometrics. First as a postdoc with Eric-Jan Wagenmakers at the Psychological Methods group, and later as an assistant professor in the same group.

He is currently working on a prestigious Veni project, developing network models for crystalized and general intelligence and analyzing network models that bridge idiographic and nomothetic psychometric approaches. Together with Mijke Rhemtulla, he is guest editor for the upcoming Psychometrika special issue on network psychometrics, and he is book review editor of Psychometrika.

In his free time, he enjoys coaching junior league basketball.

Today’s talk will be about Bayesian network psychometrics. In particular, Maarten will discuss how Bayesian model averaging solves outstanding practical and theoretical issues in contemporary network psychometric methods. 

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