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Süleyman Demir; Derya Çobanoglu Aktan; Nese Güler – International Journal of Assessment Tools in Education, 2023
This study has two main purposes. Firstly, to compare the different item selection methods and stopping rules used in Computerized Adaptive Testing (CAT) applications with simulative data generated based on the item parameters of the Vocational Maturity Scale. Secondly, to test the validity of CAT application scores. For the first purpose,…
Descriptors: Computer Assisted Testing, Adaptive Testing, Vocational Maturity, Measures (Individuals)
Wyse, Adam E.; McBride, James R. – Measurement: Interdisciplinary Research and Perspectives, 2022
A common practical challenge is how to assign ability estimates to all incorrect and all correct response patterns when using item response theory (IRT) models and maximum likelihood estimation (MLE) since ability estimates for these types of responses equal -8 or +8. This article uses a simulation study and data from an operational K-12…
Descriptors: Scores, Adaptive Testing, Computer Assisted Testing, Test Length
An Alternative Estimator for the Maximum Likelihood Estimator for the Two Extreme Response Patterns.
Samejima, Fumiko – 1981
In the methods and approaches developed for estimating the operating characteristics of the discrete item responses, the maximum likelihood estimate of the examinee based upon the "Old Test" has an important role. When Old Test does not provide a sufficient amount of test information for the upper and lower part of the ability interval,…
Descriptors: Academic Ability, Adaptive Testing, Bayesian Statistics, Error Patterns

Lin, Miao-Hsiang; Hsiung, Chao A. – Psychometrika, 1994
Two simple empirical approximate Bayes estimators are introduced for estimating domain scores under binomial and hypergeometric distributions respectively. Criteria are established regarding use of these functions over maximum likelihood estimation counterparts. (SLD)
Descriptors: Adaptive Testing, Bayesian Statistics, Computation, Equations (Mathematics)
Meijer, Rob R. – Journal of Educational Measurement, 2004
Two new methods have been proposed to determine unexpected sum scores on sub-tests (testlets) both for paper-and-pencil tests and computer adaptive tests. A method based on a conservative bound using the hypergeometric distribution, denoted p, was compared with a method where the probability for each score combination was calculated using a…
Descriptors: Probability, Adaptive Testing, Item Response Theory, Scores