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sample used in item calibration What began in the 1940s and 1950s as a goal of psychometricians such as Richardson, Tucker, Gulliksen, Lawley, and others, became reality with the important publications of Lord (1952, 1980), Rasch (1960), and Lord and Novick (1968) By the early 1970s, the theory was very much in place, some computer software was available, and applications of IRT were beginning to appear As we begin the 21st century, IRT is being used by test publishers, large testing agencies, test developers, and agencies conducting the international comparative studies of educational achievement such as TIMSS and OECD/PISA, and assessment specialists around the world are addressing technical problems such as the automated design of tests, the study of item bias, equating test scores, computer-adaptive testing, and score reporting (Hambleton & Pitoniak, 2002; Hambleton et al, 1991) Van der Linden describes, in his chapter, the next generation of technical problems that are associated with the use of new test designs and automated test assembly (van der Linden, 2005) building computer-adaptive tests with large numbers of speci cations, controlling the exposure of items, detecting cheating, and cloning test items He addresses each of these technical problems with IRT models IRT, in its basic form, postulates that (1) underlying examinee performance on a test is a single ability or trait, and (2) the relationship between the probability that an examinee will provide a correct answer (or agree with a statement, in the case of a personality or attitude survey) and the examinee s ability can be described by a monotonically increasing curve or function We would expect examinees with more ability to have a higher probability of providing a correct answer than those with less ability, or, in the case of an instrument measuring attitudes towards a topic, we would expect those persons with very positive attitudes to agree with a statement more frequently than those persons with less positive attitudes The estimation problem then is to place examinees and items on a reporting scale to maximize the agreement between the chosen model and the data itself (see Hambleton et al, 1991) It may suf ce to simply say that within an IRT measurement system ability estimates for an examinee obtained from tests that vary in dif culty will be the same, except for the expected measurement errors Some samples of items are more useful for assessing ability (those that are targeted to the ability level of the examinee), and therefore the corresponding errors associated with ability estimation will be smaller than item samples that are less optimal, but the ability parameter being estimated is the same across item sets, unlike the case in classical test theory where the person parameter of interest, true score, is test dependent Sample invariant ability parameters are of immense value in testing because tests can be matched to the ability level of examinees to minimize errors of measurement and maximize test appropriateness, while at the same time comparisons in ability scores can be made because the ability estimates are not test dependent The concepts that ability and item parameters do not change as a result of different samples of items and persons are known as ability parameter invariance and item parameter invariance, respectively In theory, this is because when.

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