Leiden University
- Bhangale, Aditi Manoj: Latent variable models for prediction
- Brystowski, Zino: Stacked Domain Learning for Interdisciplinary Theory Development
- Kloos, Kevin: Improving the Accuracy of Aggregate Statistics with Quantification Learning
- Langerak, Andrea van: Effectiveness of Self-Correction Mechanisms in Psychological Research
- Liu, Kaiwen: Linking brain function and structure to phenotypes: does more data in fixed sample size lead to higher replicability?
- 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
- Verbeij, Lisa: How trustworthy are simulation studies? A meta-research perspective
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.
- Jafarian, Nanda: Social learning in a structured world
- Johansson, Annie: Optimizing Personalized Learning at Scale by Setting Up Failure for Success
- Korthals, Luke: Automatic text evaluation and feedback
- Laskar, Pritam: Cascading Transitions in Learning
- Metwaly, Florian: Bayesian Methods for Network Analysis of Longitudinal Psychometric Data
- Nak, Jason: Connecting Phenomena to Theories in Psychological Science
- Ron, de, Jill: A Formal Theory of Fear
- Smal, Iris: Cascading Transitions in Opinions
- Sekulovski, Nikola: Bayesian Psychometric Network Modelling for Cross-sectional Data
- 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
- Heister, Hannah: What is normal? Accurate norms and their use for psychological tests
- Lortz, Sebastian: Models for Computer Adaptive Testing of Cognitive Functions
- Marquez Flores, Gustavo Andres: Motives for Statistical Reporting Practices
- Peringa, Ilse Petra: Predicting Performance in the Complex Soccer Environment
- Petersen, Fridtjof: Stress in Action: Modeling and Predicting the effects of stress
- Rimpler, Alioscha: Model Complexity in Psychology
- 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
Tilburg University
- Bouma, Anouk: How trustworthy are simulation studies? A meta-research perspective
- Constantin, Mihai: Tools for Aiding Empirical Research Based on Intensive Longitudinal Data
- Eiling, Ward: From Patterns to Principles: Machine Learning-Informed Theory Formation Methods for the Social Sciences
- Failenschmid, Jan: Non-Linear Intensive Longitudinal Methods
- Goos, Cas: Improving the Robustness of Science using Evidence-Based Journal Level Interventions
- Hoesel, Tijn van: Spin – Questionable Research Practices in Scientific Reporting
- Kao, Hsin: SEM 2.0 – Towards personalized multi-disciplinary treatment plans
- Kolesnikova, Diana: Reasoning machine in social science
- Koop, Jonathan: Advancing Relational Event Models Using Nonlinear Statistical Techniques
- Kretzler, Ben: Testing Causal Effects Variation
- Kuchina, Angelina: Psychometric innovations in monitoring learning progress in students
- Le, Tra: SEM 2.0: Towards personalized multi-disciplinary treatment plans
- Lunardelli, Ilaria: Combining estimates based on multiple datasets
- Norouzi, Rasoul: Reasoning machine in social science
- 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
- 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
- Tkachenko, Irina: Addressing Validity Threats in Models with Response Times for International Large-Scale Assessment
- Willigers, Iris: Examining Variation in Causal Effects in Psychology
- Wong, Tsz Keung: Modelling Multiverses and Correcting for P-hacking
- Wu, Yueheng (Harriet): Bayesian gaussian process for nonlinear social science
- Yang, Pengyuan (Travis): Embracing the complexity of contemporary research: The next generation of structural equation modelling tools
- Zubareva, Nataliia: Informative prior specification for nonlinear Gaussian processes
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
- Carrière, Thijs: Data quality in app based data collection
- Chvojka, Edita: Rethinking Model Fit Guidelines for Longitudinal Structural Equation
Modeling (LSEM) in Research on Youth - Dvoriak, Vira: Advanced Network Approaches to Enhance Youth Mental Health
- Edmar, Alfons: Bayesian Evidence Synthesis for informative hypotheses: Aggregating evidence from conceptual replications
- Haqiqatkhah, Manuel: Methodology of Psychological Processes
- Leeuwen, van, Florian: Getting the best predictions from complex data sets: Balancing scalability and interpretability
- Leplaa, Hidde: Replication in the behavioural sciences
- Lösener,Ulrich: Bayesian Sample Size Calculation for Multilevel Trials
- 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
- Oberman, Hanne: Computational Evaluation for Dark Data Science
- Orrizonte, Giuliana: Accelerating the development of effective psychological treatments for traumatized refugees using Bayesian methodology
- Remmerswaal, Danielle: Push-to-app: Effective recruitment and retention in smart surveys
- Rostami Charati, Aliasghar: Bayesian Penalisation and Variable Selection in Relational Event Model
- Sayed, Khadiga: Studies into Non-Saturated Randomized Response Models
- Sukpan, Chuenjai: Inequality-constrained model selection (for dynamical models)
- Vink, Pepijn: Personalized depression dynamics in emerging adults: towards a real-time alert system
- Volker, Thom: Private yet accessible: advancing privacy-aware synthetization of sensitive microdata
KU University Leuven
- Carlier, Chiara: Capturing similarity in multivariate dyadic time series: a statistical framework.
- Dickson, Shannon: Measuring the dynamic structure of affect
- Dou, Zhiwei: Statistical methods for intensive longitudinal dyadic data
- Dang Guay, Jennifer: 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
- Nowicki, Lucas: New structural equation modeling methods to find and account for measurement non-invariances
- Peeters, Lisa: Developing adaptive sampling schemes for improving the design of intensive longitudinal studies.
- Pihlajamäki, Milla: The Who, How, and What of Experience Sampling – Building an Evidence-Based Methodological Foundation for Experience-Sampling Research in Different Populations
- Revol, Jordan: Research Group of Quantitative Psychology and Individual Differences
- Szűcs, Tamás: Affectometrics – Examining and improving the validity of the measurement of affect
- 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
- Yao, Meijun: Capturing Relations among Psychological Constructs and Comparing Them Within Groups
- Yu, Kenny: Research Group of Quantitative Psychology and Individual Differences
- Zhao, Hongwei: Novel mixture SEM methods for comparing structural relations among many groups
Statistics Netherlands (CBS)
VU University Amsterdam
- Gómez-Echeverry, Santiago: Measuring the Quality of Big Data and Administrative Data