Between-Item Multidimensional IRT: How Far Can the Estimation Methods Go?

Multidimensional item response models are known to be difficult to estimate, with a variety of estimation and modeling strategies being proposed to handle the difficulties. While some previous studies have considered the performance of these estimation methods, they typically include only one or two methods, or a small number of factors. In this paper, we report on a large simulation study of between-item multidimensional IRT estimation methods, considering five different methods, a variety of sample sizes, and up to eight factors. This study provides a comprehensive picture of the methods’ relative performance, as well as each individual method’s strengths and weaknesses. The study results lead us to make recommendations for applied research, related to which estimation methods should be used under various scenarios.

IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters

A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.