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Showing 1 to 15 of 36 results Save | Export
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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)
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Wang, Shiyu; Xiao, Houping; Cohen, Allan – Journal of Educational and Behavioral Statistics, 2021
An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Computation, Robustness (Statistics)
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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
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Chun Wang; Ping Chen; Shengyu Jiang – Journal of Educational Measurement, 2020
Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait [theta] estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence, questions remain as to how to…
Descriptors: Test Construction, Test Items, Adaptive Testing, Maximum Likelihood Statistics
Wang, Chun; Chen, Ping; Jiang, Shengyu – Grantee Submission, 2019
Many large-scale educational surveys have moved from linear form design to multistage testing (MST) design. One advantage of MST is that it can provide more accurate latent trait [theta] estimates using fewer items than required by linear tests. However, MST generates incomplete response data by design; hence questions remain as to how to…
Descriptors: Adaptive Testing, Test Items, Item Response Theory, Maximum Likelihood Statistics
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Cetin-Berber, Dee Duygu; Sari, Halil Ibrahim; Huggins-Manley, Anne Corinne – Educational and Psychological Measurement, 2019
Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated…
Descriptors: Computer Assisted Testing, Adaptive Testing, Error of Measurement, Research Problems
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Sahin, Melek Gülsah; Öztürk, Nagihan Boztunç – International Journal of Assessment Tools in Education, 2019
New statistical methods are being added to the literature as a result of scientific developments each and every day. This study aims at investigating one of these, Maximum Likelihood Score Estimation with Fences (MLEF) method, in ca-MST. The results obtained from this study will contribute to both national and international literature since there…
Descriptors: Maximum Likelihood Statistics, Computation, International Assessment, Foreign Countries
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Sahin, Alper; Weiss, David J. – Educational Sciences: Theory and Practice, 2015
This study aimed to investigate the effects of calibration sample size and item bank size on examinee ability estimation in computerized adaptive testing (CAT). For this purpose, a 500-item bank pre-calibrated using the three-parameter logistic model with 10,000 examinees was simulated. Calibration samples of varying sizes (150, 250, 350, 500,…
Descriptors: Adaptive Testing, Computer Assisted Testing, Sample Size, Item Banks
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Han, Kyung T.; Guo, Fanmin – Practical Assessment, Research & Evaluation, 2014
The full-information maximum likelihood (FIML) method makes it possible to estimate and analyze structural equation models (SEM) even when data are partially missing, enabling incomplete data to contribute to model estimation. The cornerstone of FIML is the missing-at-random (MAR) assumption. In (unidimensional) computerized adaptive testing…
Descriptors: Maximum Likelihood Statistics, Structural Equation Models, Data, Computer Assisted Testing
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Ho, Tsung-Han; Dodd, Barbara G. – Applied Measurement in Education, 2012
In this study we compared five item selection procedures using three ability estimation methods in the context of a mixed-format adaptive test based on the generalized partial credit model. The item selection procedures used were maximum posterior weighted information, maximum expected information, maximum posterior weighted Kullback-Leibler…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
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Kubinger, Klaus D.; Reif, Manuel; Yanagida, Takuya – Educational Research and Evaluation, 2011
Item position effects provoke serious problems within adaptive testing. This is because different testees are necessarily presented with the same item at different presentation positions, as a consequence of which comparing their ability parameter estimations in the case of such effects would not at all be fair. In this article, a specific…
Descriptors: Adaptive Testing, Test Items, Item Analysis, Item Response Theory
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Chen, Ping; Xin, Tao; Wang, Chun; Chang, Hua-Hua – Psychometrika, 2012
Item replenishing is essential for item bank maintenance in cognitive diagnostic computerized adaptive testing (CD-CAT). In regular CAT, online calibration is commonly used to calibrate the new items continuously. However, until now no reference has publicly become available about online calibration for CD-CAT. Thus, this study investigates the…
Descriptors: Computer Assisted Testing, Adaptive Testing, Diagnostic Tests, Cognitive Tests
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He, Wei; Reckase, Mark D. – Educational and Psychological Measurement, 2014
For computerized adaptive tests (CATs) to work well, they must have an item pool with sufficient numbers of good quality items. Many researchers have pointed out that, in developing item pools for CATs, not only is the item pool size important but also the distribution of item parameters and practical considerations such as content distribution…
Descriptors: Item Banks, Test Length, Computer Assisted Testing, Adaptive Testing
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Koch, William R.; Dodd, Barbara G. – Applied Measurement in Education, 1989
Various aspects of the computerized adaptive testing (CAT) procedure for partial credit scoring were manipulated, focusing on the effects of the manipulations on operational characteristics of the CAT. The effects of item-pool size, item-pool information, and stepsizes used along the trait continuum were assessed. (TJH)
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Banks, Maximum Likelihood Statistics
Pommerich, Mary; Segall, Daniel O. – 2003
Research discussed in this paper was conducted as part of an ongoing large-scale simulation study to evaluate methods of calibrating pretest items for computerized adaptive testing (CAT) pools. The simulation was designed to mimic the operational CAT Armed Services Vocational Aptitude Battery (ASVAB) testing program, in which a single pretest item…
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Banks, Maximum Likelihood Statistics
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