The Use of Multilevel Item Response Theory Modeling in Applied Research: An Illustration

Embedding item response theory (IRT) models within a multilevel modeling framework has been shown by many authors to allow better estimation of the relationships between predictor variables and IRT latent traits (Adams, Wilson, & Wu,1997). A multilevel IRT model recently proposed by Kamata (1998, 2001) yields the additional benefit of being able to accommodate data that are collected in hierarchical settings. This expansion of multilevel IRT models to three levels allows not only the dependency typically found in hierarchical data to be accommodated, but also the estimation of (a) latent traits at different levels and (b) the relationships between predictor variables and latent traits at different levels. The purpose of this article is to provide both a description and application of Kamata's 3-level IRT model. The advantages and disadvantages of using multilevel IRT models in applied research are discussed and directions for future research are given.