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van der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 2022
Two independent statistical tests of item compromise are presented, one based on the test takers' responses and the other on their response times (RTs) on the same items. The tests can be used to monitor an item in real time during online continuous testing but are also applicable as part of post hoc forensic analysis. The two test statistics are…
Descriptors: Test Items, Item Analysis, Item Response Theory, Computer Assisted Testing
Jewsbury, Paul A.; van Rijn, Peter W. – Journal of Educational and Behavioral Statistics, 2020
In large-scale educational assessment data consistent with a simple-structure multidimensional item response theory (MIRT) model, where every item measures only one latent variable, separate unidimensional item response theory (UIRT) models for each latent variable are often calibrated for practical reasons. While this approach can be valid for…
Descriptors: Item Response Theory, Computation, Test Items, Adaptive Testing
Passos, Valeria Lima; Berger, Martijn P. F.; Tan, Frans E. S. – Journal of Educational and Behavioral Statistics, 2008
During the early stage of computerized adaptive testing (CAT), item selection criteria based on Fisher"s information often produce less stable latent trait estimates than the Kullback-Leibler global information criterion. Robustness against early stage instability has been reported for the D-optimality criterion in a polytomous CAT with the…
Descriptors: Computer Assisted Testing, Adaptive Testing, Evaluation Criteria, Item Analysis

Bradlow, Eric T.; Weiss, Robert E. – Journal of Educational and Behavioral Statistics, 2001
Compares four methods that map outlier statistics to a familiarity probability scale (a "P" value). Explored these methods in the context of computerized adaptive test data from a 1995 nationally administered computerized examination for professionals in the medical industry. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Probability, Test Construction

van Krimpen-Stoop, Edith M. L. A.; Meijer, Rob R. – Journal of Educational and Behavioral Statistics, 2001
Proposed person-fit statistics that are designed for use in a computerized adaptive test (CAT) and derived critical values for these statistics using cumulative sum (CUSUM) procedures so that item-score patterns can be classified as fitting or misfitting. Compared nominal Type I errors with empirical Type I errors through simulation studies. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Simulation, Test Construction

Bradlow, Eric T. – Journal of Educational and Behavioral Statistics, 1996
The three-parameter logistic (3-PL) model is described and a derivation of the 3-PL observed information function is presented for a single binary response from one examinee with known item parameters. Formulas are presented for the probability of negative information and for the expected information (always nonnegative). (SLD)
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Item Response Theory
van der Linden, Wim J.; Veldkamp, Bernard P. – Journal of Educational and Behavioral Statistics, 2004
Item-exposure control in computerized adaptive testing is implemented by imposing item-ineligibility constraints on the assembly process of the shadow tests. The method resembles Sympson and Hetter's (1985) method of item-exposure control in that the decisions to impose the constraints are probabilistic. The method does not, however, require…
Descriptors: Probability, Law Schools, Admission (School), Adaptive Testing
Nandakumar, Ratna; Roussos, Louis – Journal of Educational and Behavioral Statistics, 2004
A new procedure, CATSIB, for assessing differential item functioning (DIF) on computerized adaptive tests (CATs) is proposed. CATSIB, a modified SIBTEST procedure, matches test takers on estimated ability and controls for impact-induced Type 1 error inflation by employing a CAT version of the IBTEST "regression correction." The…
Descriptors: Evaluation, Adaptive Testing, Computer Assisted Testing, Pretesting
van der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 2003
The Hetter and Sympson (1997; 1985) method is a method of probabilistic item-exposure control in computerized adaptive testing. Setting its control parameters to admissible values requires an iterative process of computer simulations that has been found to be time consuming, particularly if the parameters have to be set conditional on a realistic…
Descriptors: Law Schools, Adaptive Testing, Admission (School), Computer Assisted Testing

Stocking, Martha L. – Journal of Educational and Behavioral Statistics, 1996
An alternative method for scoring adaptive tests, based on number-correct scores, is explored and compared with a method that relies more directly on item response theory. Using the number-correct score with necessary adjustment for intentional differences in adaptive test difficulty is a statistically viable scoring method. (SLD)
Descriptors: Adaptive Testing, Computer Assisted Testing, Difficulty Level, Item Response Theory

Spray, Judith A.; Reckase, Mark D. – Journal of Educational and Behavioral Statistics, 1996
Two procedures for classifying examinees into categories, one based on the sequential probability ratio test (SPRT) and the other on sequential Bayes methodology, were compared to determine which required fewer items for classification. Results showed that the SPRT procedure requires fewer items to achieve the same accuracy level. (SLD)
Descriptors: Ability, Bayesian Statistics, Classification, Comparative Analysis

Berger, Martijn P. F.; Veerkamp, Wim J. J. – Journal of Educational and Behavioral Statistics, 1997
Some alternative criteria for item selection in adaptive testing are proposed that take into account uncertainty in the ability estimates. A simulation study shows that the likelihood weighted information criterion is a good alternative to the maximum information criterion. Another good alternative uses a Bayesian expected a posteriori estimator.…
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing