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Falk, Carl F.; Feuerstahler, Leah M. – Educational and Psychological Measurement, 2022
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a…
Descriptors: Item Response Theory, Adaptive Testing, Computer Assisted Testing, Nonparametric Statistics
Yu Wang – ProQuest LLC, 2024
The multiple-choice (MC) item format has been widely used in educational assessments across diverse content domains. MC items purportedly allow for collecting richer diagnostic information. The effectiveness and economy of administering MC items may have further contributed to their popularity not just in educational assessment. The MC item format…
Descriptors: Multiple Choice Tests, Cognitive Tests, Cognitive Measurement, Educational Diagnosis
Van Norman, Ethan R.; Ysseldyke, James E. – School Psychology Review, 2020
Within multitiered systems of support, assessment practices that limit the amount of time students miss instruction should be prioritized. At the same time, decisions about student response to intervention need to be based upon technically adequate data. We evaluated the impact of data collection frequency and trend estimation method on the…
Descriptors: Data Collection, Adaptive Testing, Computer Assisted Testing, Computation
Xu, Xueli; Douglas, Jeff – Psychometrika, 2006
Nonparametric item response models have been developed as alternatives to the relatively inflexible parametric item response models. An open question is whether it is possible and practical to administer computerized adaptive testing with nonparametric models. This paper explores the possibility of computerized adaptive testing when using…
Descriptors: Simulation, Nonparametric Statistics, Item Analysis, Item Response Theory
Krass, Iosif A.; Thomasson, Gary L. – 1999
New items are being calibrated for the next generation of the computerized adaptive (CAT) version of the Armed Services Vocational Aptitude Battery (ASVAB) (Forms 5 and 6). The requirements that the items be "good" three-parameter logistic (3-PL) model items and typically "like" items in the previous CAT-ASVAB tests have…
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Nonparametric Statistics
Samejima, Fumiko – 1998
Item response theory (IRT) has been adapted as the theoretical foundation of computerized adaptive testing (CAT) for several decades. In applying IRT to CAT, there are certain considerations that are essential, and yet tend to be neglected. These essential issues are addressed in this paper, and then several ways of eliminating noise and bias in…
Descriptors: Ability, Adaptive Testing, Estimation (Mathematics), Item Response Theory
Yan, Duanli; Lewis, Charles; Stocking, Martha – Journal of Educational and Behavioral Statistics, 2004
It is unrealistic to suppose that standard item response theory (IRT) models will be appropriate for all the new and currently considered computer-based tests. In addition to developing new models, we also need to give attention to the possibility of constructing and analyzing new tests without the aid of strong models. Computerized adaptive…
Descriptors: Nonparametric Statistics, Regression (Statistics), Adaptive Testing, Computer Assisted Testing
Roussos, Louis; Nandakumar, Ratna; Cwikla, Julie – 2000
CATSIB is a differential item functioning (DIF) assessment methodology for computerized adaptive test (CAT) data. Kernel smoothing (KS) is a technique for nonparametric estimation of item response functions. In this study an attempt has been made to develop a more efficient DIF procedure for CAT data, KS-CATSIB, by combining CATSIB with kernel…
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Bias, Item Response Theory
Yan, Duanli; Lewis, Charles; Stocking, Martha – 1998
It is unrealistic to suppose that standard item response theory (IRT) models will be appropriate for all new and currently considered computer-based tests. In addition to developing new models, researchers will need to give some attention to the possibility of constructing and analyzing new tests without the aid of strong models. Computerized…
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Item Response Theory

Cliff, Norman – Psychometrika, 1979
This paper traces the course of the consequences of viewing test responses as simply providing dichotomous data concerning ordinal relations. It begins by proposing that the score matrix is best considered to be items-plus-persons by items-plus-persons, and recording the wrongs as well as the rights. (Author/CTM)
Descriptors: Adaptive Testing, Mathematical Models, Matrices, Measurement

Cliff, Norman; And Others – Applied Psychological Measurement, 1979
Monte Carlo research with TAILOR, a program using implied orders as a basis for tailored testing, is reported. TAILOR typically required about half the available items to estimate, for each simulated examinee, the responses on the remainder. (Author/CTM)
Descriptors: Adaptive Testing, Computer Programs, Item Sampling, Nonparametric Statistics
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
Samejima, Fumiko – 1990
This paper is the final report of a multi-year project sponsored by the Office of Naval Research (ONR) in 1987 through 1990. The main objectives of the research summarized were to: investigate the non-parametric approach to the estimation of the operating characteristics of discrete item responses; revise and strengthen the package computer…
Descriptors: Adaptive Testing, Computer Assisted Testing, Distractors (Tests), Equations (Mathematics)