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Zachary K. Collier; Minji Kong; Olushola Soyoye; Kamal Chawla; Ann M. Aviles; Yasser Payne – Journal of Educational and Behavioral Statistics, 2024
Asymmetric Likert-type items in research studies can present several challenges in data analysis, particularly concerning missing data. These items are often characterized by a skewed scaling, where either there is no neutral response option or an unequal number of possible positive and negative responses. The use of conventional techniques, such…
Descriptors: Likert Scales, Test Items, Item Analysis, Evaluation Methods
Joakim Wallmark; James O. Ramsay; Juan Li; Marie Wiberg – Journal of Educational and Behavioral Statistics, 2024
Item response theory (IRT) models the relationship between the possible scores on a test item against a test taker's attainment of the latent trait that the item is intended to measure. In this study, we compare two models for tests with polytomously scored items: the optimal scoring (OS) model, a nonparametric IRT model based on the principles of…
Descriptors: Item Response Theory, Test Items, Models, Scoring
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
van der Linden, Wim J.; Veldkamp, Bernard P. – Journal of Educational and Behavioral Statistics, 2007
Two conditional versions of the exposure-control method with item-ineligibility constraints for adaptive testing in van der Linden and Veldkamp (2004) are presented. The first version is for unconstrained item selection, the second for item selection with content constraints imposed by the shadow-test approach. In both versions, the exposure rates…
Descriptors: Law Schools, Adaptive Testing, Item Analysis, Probability

Zwick, Rebecca; Thayer, Dorothy T. – Journal of Educational and Behavioral Statistics, 1996
Two possible standard error formulas for the polytomous differential item functioning index proposed by N. J. Dorans and A. P. Schmitt (1991) were derived. These standard errors, and associated hypothesis-testing procedures, were evaluated through simulated data. The standard error that performed better is based on N. Mantel's (1963)…
Descriptors: Error of Measurement, Evaluation Methods, Hypothesis Testing, Item Bias
Revuelta, Javier – Journal of Educational and Behavioral Statistics, 2004
This article presents a psychometric model for estimating ability and item-selection strategies in self-adapted testing. In contrast to computer adaptive testing, in self-adapted testing the examinees are allowed to select the difficulty of the items. The item-selection strategy is defined as the distribution of difficulty conditional on the…
Descriptors: Psychometrics, Adaptive Testing, Test Items, Evaluation Methods
May, Henry – Journal of Educational and Behavioral Statistics, 2006
In this article, a new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores…
Descriptors: Item Response Theory, Bayesian Statistics, Socioeconomic Status, Scaling
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2006
Bayesian networks are frequently used in educational assessments primarily for learning about students' knowledge and skills. There is a lack of works on assessing fit of Bayesian networks. This article employs the posterior predictive model checking method, a popular Bayesian model checking tool, to assess fit of simple Bayesian networks. A…
Descriptors: Models, Educational Assessment, Diagnostic Tests, Evaluation Methods