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Ozdemir, Burhanettin; Gelbal, Selahattin – Education and Information Technologies, 2022
The computerized adaptive tests (CAT) apply an adaptive process in which the items are tailored to individuals' ability scores. The multidimensional CAT (MCAT) designs differ in terms of different item selection, ability estimation, and termination methods being used. This study aims at investigating the performance of the MCAT designs used to…
Descriptors: Scores, Computer Assisted Testing, Test Items, Language Proficiency
Lamsal, Sunil – ProQuest LLC, 2015
Different estimation procedures have been developed for the unidimensional three-parameter item response theory (IRT) model. These techniques include the marginal maximum likelihood estimation, the fully Bayesian estimation using Markov chain Monte Carlo simulation techniques, and the Metropolis-Hastings Robbin-Monro estimation. With each…
Descriptors: Item Response Theory, Monte Carlo Methods, Maximum Likelihood Statistics, Markov Processes
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Wang, Wen-Chung; Liu, Chen-Wei; Wu, Shiu-Lien – Applied Psychological Measurement, 2013
The random-threshold generalized unfolding model (RTGUM) was developed by treating the thresholds in the generalized unfolding model as random effects rather than fixed effects to account for the subjective nature of the selection of categories in Likert items. The parameters of the new model can be estimated with the JAGS (Just Another Gibbs…
Descriptors: Computer Assisted Testing, Adaptive Testing, Models, Bayesian Statistics
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He, Wei; Wolfe, Edward W. – Educational and Psychological Measurement, 2012
In administration of individually administered intelligence tests, items are commonly presented in a sequence of increasing difficulty, and test administration is terminated after a predetermined number of incorrect answers. This practice produces stochastically censored data, a form of nonignorable missing data. By manipulating four factors…
Descriptors: Individual Testing, Intelligence Tests, Test Items, Test Length
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Kieftenbeld, Vincent; Natesan, Prathiba – Applied Psychological Measurement, 2012
Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…
Descriptors: Test Length, Markov Processes, Item Response Theory, Monte Carlo Methods
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Finkelman, Matthew David – Applied Psychological Measurement, 2010
In sequential mastery testing (SMT), assessment via computer is used to classify examinees into one of two mutually exclusive categories. Unlike paper-and-pencil tests, SMT has the capability to use variable-length stopping rules. One approach to shortening variable-length tests is stochastic curtailment, which halts examination if the probability…
Descriptors: Mastery Tests, Computer Assisted Testing, Adaptive Testing, Test Length
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de la Torre, Jimmy; Song, Hao – Applied Psychological Measurement, 2009
Assessments consisting of different domains (e.g., content areas, objectives) are typically multidimensional in nature but are commonly assumed to be unidimensional for estimation purposes. The different domains of these assessments are further treated as multi-unidimensional tests for the purpose of obtaining diagnostic information. However, when…
Descriptors: Ability, Tests, Item Response Theory, Data Analysis
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Hendrawan, Irene; Glas, Cees A. W.; Meijer, Rob R. – Applied Psychological Measurement, 2005
The effect of person misfit to an item response theory model on a mastery/nonmastery decision was investigated. Furthermore, it was investigated whether the classification precision can be improved by identifying misfitting respondents using person-fit statistics. A simulation study was conducted to investigate the probability of a correct…
Descriptors: Probability, Statistics, Test Length, Simulation
Epstein, Kenneth I.; Steinheiser, Frederick H., Jr. – 1978
A multiparameter, programmable model was developed to examine the interactive influence of certain parameters on the probability of deciding that an examinee had attained a specified degree of mastery. It was applied within the simulated context of performance testing of military trainees. These parameters included: (1) the number of assumed…
Descriptors: Academic Ability, Bayesian Statistics, Cutting Scores, Hypothesis Testing
Steinheiser, Frederick H., Jr. – 1976
A computer simulation of Bayes' Theorem was conducted in order to determine the probability that an examinee was a master conditional upon his test score. The inputs were: number of mastery states assumed, test length, prior expectation of masters in the examinee population, and conditional probability of a master getting a randomly selected test…
Descriptors: Bayesian Statistics, Classification, Computer Programs, Criterion Referenced Tests
Kim, Seock-Ho; And Others – 1992
Hierarchical Bayes procedures were compared for estimating item and ability parameters in item response theory. Simulated data sets from the two-parameter logistic model were analyzed using three different hierarchical Bayes procedures: (1) the joint Bayesian with known hyperparameters (JB1); (2) the joint Bayesian with information hyperpriors…
Descriptors: Ability, Bayesian Statistics, Comparative Analysis, Equations (Mathematics)
Weiss, David J.; McBride, James R. – 1983
Monte Carlo simulation was used to investigate score bias and information characteristics of Owen's Bayesian adaptive testing strategy, and to examine possible causes of score bias. Factors investigated in three related studies included effects of item discrimination, effects of fixed vs. variable test length, and effects of an accurate prior…
Descriptors: Ability Identification, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing
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