Mixture IRT Model With a Higher-Order Structure for Latent Traits.

ABSTRACT: Mixture item response theory (IRT) models have been suggested as an efficient method of detecting the different response patterns derived from latent classes when developing a test. In testing situations, multiple latent traits measured by a battery of tests can exhibit a higher-order structure, and mixtures of latent classes may occur on different orders and influence the item responses of examinees from different classes. This study aims to develop a new class of higher-order mixture IRT models by integrating mixture IRT models and higher-order IRT models to address these practical concerns. The proposed higher-order mixture IRT models can accommodate both linear and nonlinear models for latent traits and incorporate diverse item response functions. The Rasch model was selected as the it

SUBMITTER: Huang HY 

PROVIDER: S-EPMC5965539 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature