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Wang, Chun; Chang, Hua-Hua; Boughton, Keith A. – Psychometrika, 2011
This paper first discusses the relationship between Kullback-Leibler information (KL) and Fisher information in the context of multi-dimensional item response theory and is further interpreted for the two-dimensional case, from a geometric perspective. This explication should allow for a better understanding of the various item selection methods…
Descriptors: Adaptive Testing, Item Analysis, Geometric Concepts, Item Response Theory
Chang, Yuan-chin Ivan; Lu, Hung-Yi – Psychometrika, 2010
Item calibration is an essential issue in modern item response theory based psychological or educational testing. Due to the popularity of computerized adaptive testing, methods to efficiently calibrate new items have become more important than that in the time when paper and pencil test administration is the norm. There are many calibration…
Descriptors: Test Items, Educational Testing, Adaptive Testing, Measurement
Chang, Hua-Hua; Ying, Zhiliang – Psychometrika, 2008
It has been widely reported that in computerized adaptive testing some examinees may get much lower scores than they would normally if an alternative paper-and-pencil version were given. The main purpose of this investigation is to quantitatively reveal the cause for the underestimation phenomenon. The logistic models, including the 1PL, 2PL, and…
Descriptors: Adaptive Testing, Computer Assisted Testing, Computation, Test Items
Peer reviewedLin, Miao-Hsiang; Hsiung, Chao A. – Psychometrika, 1994
Two simple empirical approximate Bayes estimators are introduced for estimating domain scores under binomial and hypergeometric distributions respectively. Criteria are established regarding use of these functions over maximum likelihood estimation counterparts. (SLD)
Descriptors: Adaptive Testing, Bayesian Statistics, Computation, Equations (Mathematics)

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