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Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi – Applied Psychological Measurement, 2013
Variable-length computerized adaptive testing (VL-CAT) allows both items and test length to be "tailored" to examinees, thereby achieving the measurement goal (e.g., scoring precision or classification) with as few items as possible. Several popular test termination rules depend on the standard error of the ability estimate, which in turn depends…
Descriptors: Adaptive Testing, Computer Assisted Testing, Test Length, Ability
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Wang, Chun; Chang, Hua-Hua; Boughton, Keith A. – Applied Psychological Measurement, 2013
Multidimensional computerized adaptive testing (MCAT) is able to provide a vector of ability estimates for each examinee, which could be used to provide a more informative profile of an examinee's performance. The current literature on MCAT focuses on the fixed-length tests, which can generate less accurate results for those examinees whose…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Length, Item Banks
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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
Mislevy, Robert J.; Wu, Pao-Kuei – 1988
The basic equations of item response theory provide a foundation for inferring examinees' abilities and items' operating characteristics from observed responses. In practice, though, examinees will usually not have provided a response to every available item--for reasons that may or may not have been intended by the test administrator, and that…
Descriptors: Ability, Adaptive Testing, Equations (Mathematics), Estimation (Mathematics)
Yi, Qing; Wang, Tianyou; Ban, Jae-Chun – 2000
Error indices (bias, standard error of estimation, and root mean square error) obtained on different scales of measurement under different test termination rules in a computerized adaptive test (CAT) context were examined. Four ability estimation methods were studied: (1) maximum likelihood estimation (MLE); (2) weighted likelihood estimation…
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Error of Measurement
Kim, Haeok; Plake, Barbara S. – 1993
A two-stage testing strategy is one method of adapting the difficulty of a test to an individual's ability level in an effort to achieve more precise measurement. A routing test provides an initial estimate of ability level, and a second-stage measurement test then evaluates the examinee further. The measurement accuracy and efficiency of item…
Descriptors: Ability, Adaptive Testing, Comparative Testing, Computer Assisted Testing
Rudner, Lawrence M. – 1978
Tailored testing provides the same information as group-administered standardized tests, but can do so using fewer items because the items administered are selected for the ability of the individual student. Thus, tailored testing offers several advantages over traditional methods. Because individual tailored tests are not timed, anxiety is…
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing
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Bergstrom, Betty A.; And Others – Applied Measurement in Education, 1992
Effects of altering test difficulty on examinee ability measures and test length in a computer adaptive test were studied for 225 medical technology students in 3 test difficulty conditions. Results suggest that, with an item pool of sufficient depth and breadth, acceptable targeting to test difficulty is possible. (SLD)
Descriptors: Ability, Adaptive Testing, Change, College Students
Spray, Judith A.; Reckase, Mark D. – 1994
The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing