An Application of Multilevel Mixture Item Response Theory Model

Abstract

Although the mixture item response theory (IRT) models are useful for heterogeneous samples, they are not capable of handling a multilevel structure that is very common in education and causes dependency between hierarchies. Ignoring the hierarchical structure may yield less accurate results because of violation of the local independence assumption. This interdependency can be modeled straightforwardly in a multi-level framework. In this study, a large-scale data set, TEOG exam, was analyzed with a multilevel mixture IRT model to account for dependency and heterogeneity in the data set. Sixteen different multilevel models (different class solutions) were estimated using the eighth-grade mathematics data set. Model fit statistics for these 16 models suggested the CB1C4 model (one school-level and four student-level latent classes) was the best fit model. Based on CB1C4 model, the students were classified into four latent student groups and one latent school group. Parameter estimates obtained with maximum likelihood estimation were presented and interpreted. Several suggestions were made based on the results.