Computerized adaptive testing: A mixture item selection approach for constrained situations

Chi Keung Eddie LEUNG, Hua Hua CHANG, Kit Tai HAU

Research output: Contribution to journalArticlespeer-review

14 Citations (Scopus)


In computerized adaptive testing (CAT), traditionally the most discriminating items are selected to provide the maximum information so as to attain the highest efficiency in trait (u) estimation. The maximum information (MI) approach typically results in unbalanced item exposure and hence high item-overlap rates across examinees. Recently, Yi and Chang (2003) proposed the multiple stratification (MS) method to remedy the shortcomings of MI. In MS, items are first sorted according to content, then difficulty and finally discrimination parameters. As discriminating items are used strategically, MS offers a better utilization of the entire item pool. However, for testing with imposed non-statistical constraints, this new stratification approach may not maintain its high efficiency. Through a series of simulation studies, this research explored the possible benefits of a mixture item selection approach (MS-MI), integrating the MS and MI approaches, in testing with non-statistical constraints. In all simulation conditions, MS consistently outperformed the other two competing approaches in item pool utilization, while the MS–MI and the MI approaches yielded higher measurement efficiency and offered better conformity to the constraints. Furthermore, the MS–MI approach was shown to perform better than MI on all evaluation criteria when control of item exposure was imposed. Copyright © 2005 The British Psychological Society.
Original languageEnglish
Pages (from-to)239-257
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number2
Publication statusPublished - Nov 2005


Leung, C.-K., Chang, H.-H., & Hau, K.-T. (2005). Computerized adaptive testing: A mixture item selection approach for constrained situations. British Journal of Mathematical and Statistical Psychology, 58(2), 239-257. doi: 10.1348/000711005X62945


  • Educational Evaluation


Dive into the research topics of 'Computerized adaptive testing: A mixture item selection approach for constrained situations'. Together they form a unique fingerprint.