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Direct linkJean-Paul Fox – Journal of Educational and Behavioral Statistics, 2025
Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…
Descriptors: Sample Size, Item Response Theory, Accuracy, Bayesian Statistics
Paganin, 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
