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Bayesian Diagnostics for Test Design and Analysis

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dc.contributor.author Silva, R. M.
dc.contributor.author Guan, Y.
dc.contributor.author Swartz, T. B.
dc.date.accessioned 2017-08-21T08:09:41Z
dc.date.available 2017-08-21T08:09:41Z
dc.date.issued 2017-07
dc.identifier.citation Silva R. M., Guan Y., & Swartz T. B. (2017). Bayesian Diagnostics for Test Design and Analysis. Journal on Efficiency and Responsibility in Education and Science, 10(2), 44-50 en_US, si_LK
dc.identifier.issn 1803-1617 (online)
dc.identifier.issn 2336-2375 (print)
dc.identifier.uri http://dr.lib.sjp.ac.lk/handle/123456789/5464
dc.description.abstract This paper attempts to bridge the gap between classical test theory and item response theory. It is demonstrated that the familiar and popular statistics used in classical test theory can be translated into a Bayesian framework where all of the advantages of the Bayesian paradigm can be realized. In particular, prior opinion can be introduced and inferences can be obtained using posterior distributions. In classical test theory, inferential decisions are based on the values of statistics that are calculated from the responses of subjects over various test questions. In the proposed approach, analogous “statistics” are constructed from the output of simulation from the posterior distribution. This leads to population- based inferences which focus on the properties of the test rather than the performance of specific subjects. The use of the JAGS programming language facilitates extensions to more complex scenarios involving the assessment of tests and questionnaires. en_US, si_LK
dc.language.iso en en_US, si_LK
dc.publisher Czech University of Life Sciences Prague en_US, si_LK
dc.subject Classical test theory en_US, si_LK
dc.subject Empirical Bayes en_US, si_LK
dc.subject Item response theory en_US, si_LK
dc.subject Markov chain Monte Carlo en_US, si_LK
dc.subject JAGS programming language en_US, si_LK
dc.title Bayesian Diagnostics for Test Design and Analysis en_US, si_LK
dc.type Article en_US, si_LK


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