5 papers
Reinforcement Learning Measurement Model
Wenqian Xu, Feng Ji
Interactive assessments generate sequential process data that are not well handled by conventional item response models. Existing MDP-based measurement approaches, such as the Mark…
LLM-generated personalized nudges for improving pro-environmental behavior: Field evidence from resource conservation
Zonghan Li, Yi Liu, Chunyan Wang +3
Encouraging pro-environmental behavior remains a major challenge for sustainable cities. Conventional feedback nudges can show individuals how their current behavior compares with…
Robust Standard Errors for Bayesian Posterior Functionals via the Infinitesimal Jackknife
Nanyu Luo, Feng Ji
Quantitative research in the social and behavioral sciences relies heavily on nonlinear posterior functionals such as indirect effects, standardized coefficients, effect sizes, int…
Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
Biying Zhou, Nanyu Luo, Feng Ji
Item Response Theory (IRT) models are widely used to estimate respondents' latent abilities and calibrate item difficulty. Traditional IRT estimation typically requires centralizin…
Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm
Nanyu Luo, Feng Ji
Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accura…