JMASM28: Gibbs Sampling for 2PNO Multi-unidimensional Item Response Theory Models (Fortran)

JMASM27: An Algorithm for Implementing Gibbs Sampling for 2PNO IRT Models (Fortran)

A High Performance Gibbs Sampling Algorithm for Item Response Theory

Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computationally expensive, the procedure is limited in real applications. It is hence important to seek ways to reduce the execution time, and therefore, a suitable solution is...

Comparing Multiunidimensional and Unidimensional Item Response Theory Models

Application of Bayesian inference using Gibbs sampling to item-response theory modeling of multi-symptom genetic data.

Several "genetic" item-response theory (IRT) models are fitted to the responses of 1086 adolescent female twins to the 33 multi-category item Mood and Feeling Questionnaire relating to depressive symptomatology in adolescence. A Markov-chain Monte Carlo (MCMC) algorithm is used within a Bayesian framework for inference using Gibbs sampling, implemented in the program WinBUGS 1.4. The final mode...

Randomized Item Response Theory Models

The randomized response (RR) technique is often used to obtain answers on sensitive questions. A new method is developed to measure latent variables using the RR technique because direct questioning leads to biased results. Within the RR technique is the probability of the true response modeled by an item response theory (IRT) model. The RR technique links the observed item response with the tr...