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Johnson, Timothy R. – Applied Psychological Measurement, 2013
One of the distinctions between classical test theory and item response theory is that the former focuses on sum scores and their relationship to true scores, whereas the latter concerns item responses and their relationship to latent scores. Although item response theory is often viewed as the richer of the two theories, sum scores are still…
Descriptors: Item Response Theory, Scores, Computation, Bayesian Statistics
Lee, Jihyun; Corter, James E. – Applied Psychological Measurement, 2011
Diagnosis of misconceptions or "bugs" in procedural skills is difficult because of their unstable nature. This study addresses this problem by proposing and evaluating a probability-based approach to the diagnosis of bugs in children's multicolumn subtraction performance using Bayesian networks. This approach assumes a causal network relating…
Descriptors: Misconceptions, Probability, Children, Subtraction
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
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

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

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