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Abu-Ghazalah, Rashid M.; Dubins, David N.; Poon, Gregory M. K. – Applied Measurement in Education, 2023
Multiple choice results are inherently probabilistic outcomes, as correct responses reflect a combination of knowledge and guessing, while incorrect responses additionally reflect blunder, a confidently committed mistake. To objectively resolve knowledge from responses in an MC test structure, we evaluated probabilistic models that explicitly…
Descriptors: Guessing (Tests), Multiple Choice Tests, Probability, Models
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Lúcio, Patrícia Silva; Vandekerckhove, Joachim; Polanczyk, Guilherme V.; Cogo-Moreira, Hugo – Journal of Psychoeducational Assessment, 2021
The present study compares the fit of two- and three-parameter logistic (2PL and 3PL) models of item response theory in the performance of preschool children on the Raven's Colored Progressive Matrices. The test of Raven is widely used for evaluating nonverbal intelligence of factor g. Studies comparing models with real data are scarce on the…
Descriptors: Guessing (Tests), Item Response Theory, Test Validity, Preschool Children
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Culpepper, Steven Andrew – Journal of Educational and Behavioral Statistics, 2015
A Bayesian model formulation of the deterministic inputs, noisy "and" gate (DINA) model is presented. Gibbs sampling is employed to simulate from the joint posterior distribution of item guessing and slipping parameters, subject attribute parameters, and latent class probabilities. The procedure extends concepts in Béguin and Glas,…
Descriptors: Bayesian Statistics, Models, Sampling, Computation
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Morrison, Donald G.; Brockway, George – Psychometrika, 1979
A modified beta binomial model is presented for use in analyzing random guessing multiple choice tests and taste tests. Detection probabilities for each item are distributed beta across the population subjects. Properties for the observable distribution of correct responses are derived. Two concepts of true score estimates are presented.…
Descriptors: Bayesian Statistics, Guessing (Tests), Mathematical Models, Multiple Choice Tests
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Jensema, Carl J. – Applied Psychological Measurement, 1977
Owen's Bayesian tailored testing method is introduced along with a brief review of its derivation. The characteristics of a good item bank are outlined and explored in terms of their influence on the Bayesian tailoring process. (Author/RC)
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Oriented Programs
Reckase, Mark D. – 1979
This paper describes two procedures for making binary classification decisions using tailored testing: the sequential probability ratio test (SPRT) and a Bayesian decision procedure. The first procedure described, the SPRT, was developed by Wald for quality control work. It has not been widely applied for testing applications because the…
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Assisted Testing, Criterion Referenced Tests
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Civil Service Commission, Washington, DC. Personnel Research and Development Center. – 1976
This pamphlet reprints three papers and an invited discussion of them, read at a Division 5 Symposium at the 1975 American Psychological Association Convention. The first paper describes a Bayesian tailored testing process and shows how it demonstrates the importance of using test items with high discrimination, low guessing probability, and a…
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Oriented Programs, Computer Programs