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Direct linkPaganin, Sally; Paciorek, Christopher J.; Wehrhahn, Claudia; Rodríguez, Abel; Rabe-Hesketh, Sophia; de Valpine, Perry – Journal of Educational and Behavioral Statistics, 2023
Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures (DPMs) offer a more flexible representation of the unknown distribution of the latent trait. However, the use of such models in the IRT…
Descriptors: Bayesian Statistics, Item Response Theory, Guidance, Evaluation Methods
Kalkan, Ömür Kaya – Measurement: Interdisciplinary Research and Perspectives, 2022
The four-parameter logistic (4PL) Item Response Theory (IRT) model has recently been reconsidered in the literature due to the advances in the statistical modeling software and the recent developments in the estimation of the 4PL IRT model parameters. The current simulation study evaluated the performance of expectation-maximization (EM),…
Descriptors: Comparative Analysis, Sample Size, Test Length, Algorithms
