Current Students

Leiden University

  • Brystowski, Zino: Stacked Domain Learning for Interdisciplinary Theory Development
  • Deen, Mathijs: Resampling methodology for longitudinal data analysis
  • Durieux, Jeffrey: Clusterwise Independent Component Analysis for multi-subject (resting state) fMRI data
  • Kloos, Kevin: Improving the Accuracy of Aggregate Statistics with Quantification Learning
  • Liu, Yuqi: Stepwise estimation approaches of growth mixture models
  • Pratiwi, Bunga Citra: Predictive Validity of Psychological Tests from a Statistical Learning Perspective
  • Rieble, Carlotta: Early Warning Signals of Depression Onset
  • Spadaccini, Giorgio: Enhancing the use and interpretation of tree-based prediction models
  • Van Loon, Wouter: Stacked Domain Learning for multi-domain data: A new ensemble method
  • Veenman, Myrthe: Communicating Networks

University of Amsterdam

  • Bilici, Zeynep: Dependent effect sizes in meta-analytical structural equation modeling (MASEM)
  • Boot, Jesse: Cascading Transitions in the psychosocial sciences (addiction)
  • Ertekin, Seyma: Cognitive Modeling Meets Educational Data Science
  • Groot, Lennert: Winner takes all: Applying MASEM with raw data”, part of the NWO VIDI research project “No data left behind. New meta-analytic structural equation models for complex data structures.
  • Finneman, Adam: Theory Construction in Complex Psychological Systems
  • Hoekstra, Ria: Within, Between and Beyond: A network perspective on intra- versus inter-individual data
  • Jonge, Hannelies: Meta-Analytic Structural Equation Modeling with Group Data
  • Keetelaar, Sara: Bayesian Psych:ometric Network Modelling for Longitudinal Data
  • Kucharsky, Simon: Inferring cognitive strategies from eye movements: A Bayesian model-based approach
  • Lunansky, Gabriela: A theoretical network model of psychological resilience
  • Nak, Jason: Connecting Phenomena to Theories in Psychological Science
  • Ron, de, Jill: A Formal Theory of Fear
  • Sekulovski, Nikola: Bayesian Psychometric Network Modelling for Cross-sectional Data
  • Tio, Pia: SPANC: Simultaneous Principal and Network Components model for integration of multi-source data
  • Van den Ende, Maarten: Computational modelling of psychological and social dynamics and urban mental health conditions
  • Veldkamp, Karel: Towards Psychometrically Interpretable Neural Networks: Bridging the Gap between Latent Variable Models from Psychometrics and Neural Networks from Deep Learning
  • Waaijers, Meike: AI-Assisted Network Construction Methods
  • Wit, de, Kay: Defining intimidation: empirical evidence for an aggressive interpersonal phenomenon

University of Groningen

  • Burger, Julian: Advancing Personalized Network Modelling in Clinical Practice
  • Heister, Hannah: What is normal? Accurate norms and their use for psychological tests.
  • Langener, Anna: Using multimodal data to understand the social environment and its effect on wellbeing
  • Linde, Maximilian: Back to Bayesics: Solving the Reproducibility Crisis in Biomedicine
  • Muradchanian, Jasmine: Improving replicability in science
  • Peringa, Ilse Petra:  Predicting Performance in the Complex Soccer Environment
  • Stadel, Marie: Capturing a Patient’s Context: Developing experience sampling tools for personalised patient feedback in psychotherapy
  • Vries, de, Klazien: What is normal? Accurate norms and their use for psychological tests
  • Zhang, Yong: Quality assessment of network models in clinical research and practice using predictive accuracy analysis

University of Twente

  • Klotzke, Konrad: Marginal Joint-Modelling of Response Accuracy and Response Times

Tilburg University

  • Arena, Giuseppe: The Time is Now: Understanding Social Network Dynamics Using Relational Event Histories
  • Augusteijn, Hilde: Getting it right with meta-analysis: Assessing heterogeneity and moderator effects in the presence of publication bias and p-hacking
  • Clouth, Felix: Personalised Treatment Options Model
  • Constantin, Mihai: Tools for Aiding Empirical Research Based on Intensive Longitudinal Data
  • Costantini, Eduardo: Treatment of Missing Data in High-Dimensional Social and Behavioral Science Data
  • Crompvoets, Elise: Pairwise comparisons within education
  • Failenschmid, Jan: Non-Linear Intensive Longitudinal Methods
  • Goos, Cas: Improving the Robustness of Science using Evidence-Based Journal Level Interventions
  • Guerra Urzola, Rosember: A huge scale optimization approach to joint data modeling in the social and behavioral sciences
  • Kuchina, Angelina: Psychometric innovations in monitoring learning progress in students
  • Le, Tra: SEM 2.0: Towards personalized multi-disciplinary treatment plans
  • Liu, Anne: Correcting for selectivity in datasets
  • Maassen, Esther: Structural equation modeling as an antidote to selective outcome reporting
  • Meijerink, Marlyne: Confirmatory methods for time-sensitive social processes
  • Norouzi, Rasoul: Reasoning machine in social science
  • Olsson-Collentine, Anton: (Data-dependent) choices and (statistical) consequences in psychology
  • Peereboom, Sanne: Assessing the artificial mind through natural language processing and psychometrics
  • Peng, Dennis: From Degrees of Freedom to Robustness: Strengthening the Evidence Base for Psychological Interventions
  • Perez Alonso, Andres: Mixture multigroup Structural Equation Modeling for comparing latent variable relations among many groups
  • Park, Soogeun: Big data in the social sciences: Statistical methods for multi-source high-dimensional data
  • Rüffer, Franziska: Moderator Analysis in Meta-Analysis
  • Rein, Manuel: New SEM methods for validly comparing structural relations over individuals and time
  • Schoenmaker, Martijn: Modeling Response Styles Behaviors in a Cross-cultural Context
  • Sibbald, Lisette: Prevention is Better Than Cure: Predicting the Onset of Postpartum Depression Using Early Warning Signals
  • Van den Akker, Olmo: Preregistration and the “failed study”
  • Wong, Tsz Keung: Modelling Multiverses and Correcting for P-hacking
  • Yuan, Shuai: Identifying Group Differences in Large-Scale Multi-Block Data

Utrecht University

  • Andresen, Pia: Coming-of-Age of Process Research: Connecting Theory with Measurement and Modelling (OPTIMAL)
  • Arts, Ingrid: Increasing MI by introducing webprobing into a BSEM
  • Barragan Ibanez, Camila: Bayesian sample size calculation for trials with multilevel data
  • Behbahani, Mohammad: Exploring Hidden Patterns in Relational Event History Data: an extension of Hidden Markov Model for the Relational Event Model
  • Berkhout, Sophie: Intensive Longitudinal Methodology
  • Chvojka, Edita: Rethinking Model Fit Guidelines for Longitudinal Structural Equation
    Modeling (LSEM) in Research on Youth
  • De Jong, Daan: OPTIMAL
  • Edmar, Alfons: Bayesian Evidence Synthesis for informative hypotheses: Aggregating evidence from conceptual replications
  • Fang, Qixiang: Content Validity of High-Dimensional Measurement
  • Haqiqatkhah, Manuel: Methodology of Psychological Processes
  • Leplaa, Hidde: Replication in the behavioural sciences
  • Lösener,Ulrich: Bayesian Sample Size Calculation for Multilevel Trials
  • Mildiner Moraga, Sebastián: The multilevel explicit-duration hidden Markov model for real time behavioural data
  • Moazeni, Mehran: Developing and applying machine learning algorithms to improve prediction in patients with heart failure
  • Mohammadi, Hadi: Explainable NLP with Human-AI Collaboration in Social Science
  • Mulder, Jeroen: Concerning Causes: Evaluation of Methods to Study Causes and Their Effects in Developmental Processes
  • Oberman, Hanne: Computational Evaluation for Dark Data Science
  • Remmerswaal, Danielle: Push-to-app: Effective recruitment and retention in smart surveys
  • Sayed, Khadiga: Studies into Non-Saturated Randomized Response Models
  • Sukpan, Chuenjai: Inequality-constrained model selection (for dynamical models)
  • Volker, Thom: Private yet accessible: advancing privacy-aware synthetization of sensitive microdata

KU University Leuven

  • Ariens, Sigert: Putting affective dynamics into context
  • Chiara Carlier
  • Claesen, Aline: Methods for estimating and improving the replicability of psychological science
  • Cloos, Leonie: Improving the measurement of mood and mood disorder
  • Dou, Zhiwei: Statistical methods for intensive longitudinal dyadic data
  • Guay, Jennifer Dang: Comparing Relations among Psychological Constructs Across Groups
  • Ji, Yuanyuan: Using measurement bursts in ESM to capture affect regulation
  • Lin, Tzu-Yao: Can we trust our numbers? Quantification of measurement reliability for intensive longitudinal data
  • Niemeijer, Koen: Tracking mood and mood disorder using mobile sensing
  • Peeters, Lisa: Developing adaptive sampling schemes for improving the design of intensive longitudinal studies.
  • Piot, Maarten: Improving the user experience of the innovative blended care platform m-Path
  • Revol, Jordan
  • Schat, Evelien: Online detection of early warning signals of mood disorder through statistical process control
  • Szűcs, Tamás: Affectometrics – Examining and improving the validity of the measurement of affect
  • Vanhasbroeck, Niels: Testing a mathematical model of affect dynamics
  • Vermeiren, Hanke: Statistical /Psychometrical techniques for digital learning applications
  • Wang, Shiyao: Statistical methods for capturing transmission and synchronization processes in triads
  • Wu, Yufei: Validity of Self-Reported Affect Ratings
  • Yu, Kenny
  • Zhao, Hongwei: Novel mixture SEM methods for comparing structural relations among many groups

Statistics Netherlands (CBS)

VU University Amsterdam

Maastricht University

Wageningen University

Erasmus University Rotterdam

In Memoriam

Janneke de Kort