Computerized classification testing under the higher-order polytomous IRT model

Kung Hsien LEE, Wen Chung WANG

Research output: Contribution to conferencePapers

Abstract

Latent traits in the human sciences may have a hierarchical structure, for example, a second-order latent trait “language proficiency” covers four first-order latent traits: listening, speaking, reading, and writing. The higher-order IRT model has been developed to account for tests measuring hierarchical latent traits. Being an IRT model, computerized classification testing (CCT) and computerized adaptive testing (CAT) under the higher-order IRT model can be developed. In this study, we focused on the development of CCT algorithms. Specifically, item responses on the first-order latent traits were assumed to follow the generalized partial credit model. The current-estimate approach was adopted for item selection and the ability-confidence-interval approach was adopted for classification. The results of simulation showed that the CCT algorithms were efficient; the higher correlation between the first-order latent traits, the more points each item had, the fewer categories for classification, the more accurate and efficient the classification would be.
Original languageEnglish
Publication statusPublished - 2011

Citation

Lee, K.-H., & Wang, W. C. (2011, July). Computerized classification testing under the higher-order polytomous IRT model. Paper presented at the 76th Annual and the 17th International Meeting of the Psychometric Society, The Hong Kong Institute of Education, China.

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