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A General Bayesian Model for Testlets: Theory and Applications.
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Wang, Xiaohui; Bradlow, Eric T.; Wainer, Howard – Applied Psychological Measurement, 2002
Proposes a modified version of commonly employed item response models in a fully Bayesian framework and obtains inferences under the model using Markov chain Monte Carlo techniques. Demonstrates use of the model in a series of simulations and with operational data from the North Carolina Test of Computer Skills and the Test of Spoken English…
Descriptors: Bayesian Statistics, Item Response Theory, Markov Processes, Mathematical Models
Some Observations on the Metric of PC-BILOG Results.
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Baker, Frank B. – Applied Psychological Measurement, 1990
The equating of results from the PC-BILOG computer program to an underlying metric was studied through simulation when a two-parameter item response theory model was used. Results are discussed in terms of the identification problem and implications for test equating. (SLD)
Descriptors: Bayesian Statistics, Computer Simulation, Equated Scores, Item Response Theory
An Empirical Study of the Effects of Small Datasets and Varying Prior Variances on Item Parameter Estimation in BILOG.
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Harwell, Michael R.; Janosky, Janine E. – Applied Psychological Measurement, 1991
Investigates the BILOG computer program's ability to recover known item parameters for different numbers of items, examinees, and variances of the prior distributions of discrimination parameters for the two-parameter logistic item-response theory model. For samples of at least 250 examinees and 15 items, simulation results support using BILOG.…
Descriptors: Bayesian Statistics, Computer Simulation, Estimation (Mathematics), Item Response Theory
The Use of Prior Distributions in Marginalized Bayesian Item Parameter Estimation: A Didactic.
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Harwell, Michael R.; Baker, Frank B. – Applied Psychological Measurement, 1991
Previous work on the mathematical and implementation details of the marginalized maximum likelihood estimation procedure is extended to encompass the marginalized Bayesian procedure for estimating item parameters of R. J. Mislevy (1986) and to communicate this procedure to users of the BILOG computer program. (SLD)
Descriptors: Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics), Item Response Theory
Computerized Mastery Testing with Nonequivalent Testlets.
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Sheehan, Kathleen; Lewis, Charles – Applied Psychological Measurement, 1992
A procedure is introduced for determining the effect of testlet nonequivalence on operating characteristics of a testlet-based computerized mastery test (CMT). The procedure, which involves estimating the CMT decision rule twice with testlet likelihoods treated as equivalent or nonequivalent, is demonstrated with testlet pools from the Architect…
Descriptors: Bayesian Statistics, Computer Assisted Testing, Computer Simulation, Equations (Mathematics)
Using Bayesian Decision Theory to Design a Computerized Mastery Test.
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Lewis, Charles; Sheehan, Kathleen – Applied Psychological Measurement, 1990
A theoretical framework for mastery testing based on item response theory and Bayesian decision theory is described and illustrated. Implementation depends on the availability of (1) a computerized test delivery system; (2) a pool of pretested items; and (3) a model relating observed test performance to true mastery status. (SLD)
Descriptors: Bayesian Statistics, Computer Assisted Testing, Equations (Mathematics), Graphs
An IRT Approach to Constructing and Scoring Pairwise Preference Items Involving Stimuli on Different Dimensions: The Multi-Unidimensional Pairwise-Preference Model
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Stark, Stephen; Chernyshenko, Oleksandr S.; Drasgow, Fritz – Applied Psychological Measurement, 2005
This article proposes an item response theory (IRT) approach to constructing and scoring multidimensional pairwise preference items. Individual statements are administered and calibrated using a unidimensional single-stimulus model. Tests are created by combining multidimensional items with a small number of unidimensional pairings needed to…
Descriptors: Test Construction, Scoring, Mathematical Models, Item Response Theory