Marjolein Fokkema is an expert on machine learning and latent variable modeling. Since 2015, she is an assistant professor at Leiden University. She completed her PhD thesis at the Vrije Universiteit Amsterdam, on how we can improve measurement and prediction in psychology. For example, how can we assess whether pre- and post-assessments in clinical trials measure the same construct in the same way? Or how can we best predict which individuals have a high or low risk of developing a psychological disorder?
In addition to researching and teaching at Leiden University, Fokkema is an associate editor at The European Journal of Psychological Assessment and a member of the Dutch Comittee on Testing Matters (COTAN) of the Dutch Intitute for Psychologists (NIP).
Marjolein Fokkema's research centers around developing statistical and machine learning algorithms that provide transparent and easy-to-understand results, which can be easily applied in decision making. Many statistical methods require lots of information and calculations, making it difficult for doctors or psychologists to apply these methods in practice. Marjolein Fokkema develops so-called decision-tree methods which, as their name already suggests, yield decision trees instead of mathematical formulas. Such decision trees can provide results that are as accurate as more complex statistical models, but they are easier to understand and require less information for making a decision.
Marjolein Fokkema teaches several courses in the Psychology curricula: In the bachelor's programme, she teaches Psychometrics. In the (research) master's programme, she teaches Latent Variable Models, as well as Statistical Learning and Prediction.
- 2017: Swiss National Science Foundation (SNF) - International Visit Grant (main applicant)
- 2017: ZonMW - Onderzoeksprogramma GGZ Middellange termijn (co-applicant)
- 2014: Psychometric Society - Student Travel Award (main application)