Related Experiment Video
Updated: Jul 29, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
Published on: June 25, 2019
A nonlinear mixed model framework for item response theory
Frank Rijmen1, Francis Tuerlinckx, Paul De Boeck
1Department of Psychology, Katholieke Universiteit Leuven, Belgium. frank.rijmen@psy.kuleuven.ac.be
Abstract:
Mixed models take the dependency between observations based on the same cluster into account by introducing 1 or more random effects. Common item response theory (IRT) models introduce latent person variables to model the dependence between responses of the same participant. Assuming a distribution for the latent variables, these IRT models are formally equivalent with nonlinear mixed models. It is shown how a variety of IRT models can be formulated as particular instances of nonlinear mixed models. The unifying framework offers the advantage that relations between different IRT models become explicit and that it is rather straightforward to see how existing IRT models can be adapted and extended. The approach is illustrated with a self-report study on anger.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model
Dose Response Curve: Conventional Versus Nonmonotonic
Friedman Two-way Analysis of Variance by Ranks
Response Surface Methodology
The process of RSM involves several key steps:
Methods of Medium Optimization

