Netherlands Institute for Advanced Study in the Humanities and Social Sciences
Netherlands
fellow
Mark de Rooij
2024-2025
Home institution
Leiden University
Country of origin (home institution)
Netherlands
Discipline(s)
Mathematics
Psychology, psychiatry and psychoanalysis
Social Sciences
Theme(s)
Behavior & Cognition
Fellowship dates
›
Biography
I am a full professor of Artificial Intelligence & Data Theory at the Institute of Psychology of Leiden University.
I studied psychology at the University of Groningen where I received a master degree in Work, Organizational, and Personnel Psychology as well as in Methodology and Statistics.
In 1996 I started as a PhD student in the Data Theory department at Leiden University studying Multidimensional Scaling methodology for transition frequency data under the supervision of Prof. dr. Willem Heiser.
On the 5th of April 2001 I received my PhD (Cum Laude). At that moment I was a Post-doc at the Methodology and Statistics unit of the Psychological Institute, where I became assistant professor in 2002 and associate professor in 2011. In 2014 I became full professor Methodology and Statistics of Psychological Research. In 2022 I was appointed on the chair AI & Data Theory.
Research Project
Logistic multidimensional data analysis
Research question: How can we analyse and visualise complex data sets with multiple categorical variables?
Multivariate data are often analyzed using techniques like principal component analysis and multidimensional unfolding. Both methods require numeric data, meaning the variables should be measured on an interval or ratio scale. However, in the social and behavioural sciences, measurements are often categorical, such as binary, ordinal, or nominal. For categorical data, logistic models are the most useful.
Over the past two decades, Mark de Rooij has been developing logistic multidimensional data analysis techniques. In this project, he plans to write a book and create accompanying software to make these tools accessible to a wider scientific audience.
Research Interests:
Predictive Psychometrics; Regression models for categorical response variables; Longitudinal Data Analysis