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 language | English |
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Publication status | Published - 2011 |
Event | The 76th Annual Meeting and 17th International Meeting of the Psychometric Society - The Hong Kong Institute of Education, Hong Kong, China Duration: 19 Jul 2011 → 22 Jul 2011 |
Conference
Conference | The 76th Annual Meeting and 17th International Meeting of the Psychometric Society |
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Abbreviated title | IMPS2011 |
Country/Territory | China |
City | Hong Kong |
Period | 19/07/11 → 22/07/11 |