@article {401,
	title = {Assembling a computerized adaptive testing item pool as a set of linear tests},
	journal = {Journal of Educational and Behavioral Statistics},
	volume = {31},
	number = {1},
	year = {2006},
	pages = {81-99},
	publisher = {Sage Publications: US},
	abstract = {Test-item writing efforts typically results in item pools with an undesirable correlational structure between the content attributes of the items and their statistical information. If such pools are used in computerized adaptive testing (CAT), the algorithm may be forced to select items with less than optimal information, that violate the content constraints, and/or have unfavorable exposure rates. Although at first sight somewhat counterintuitive, it is shown that if the CAT pool is assembled as a set of linear test forms, undesirable correlations can be broken down effectively. It is proposed to assemble such pools using a mixed integer programming model with constraints that guarantee that each test meets all content specifications and an objective function that requires them to have maximal information at a well-chosen set of ability values. An empirical example with a previous master pool from the Law School Admission Test (LSAT) yielded a CAT with nearly uniform bias and mean-squared error functions for the ability estimator and item-exposure rates that satisfied the target for all items in the pool. },
	keywords = {Algorithms, computerized adaptive testing, item pool, linear tests, mathematical models, statistics, Test Construction, Test Items},
	isbn = {1076-9986 (Print)},
	author = {van der Linden, W. J. and Ariel, A. and Veldkamp, B. P.}
}
@article {399,
	title = {Equating scores from adaptive to linear tests},
	journal = {Applied Psychological Measurement},
	volume = {30},
	number = {6},
	year = {2006},
	pages = {493-508},
	publisher = {Sage Publications: US},
	abstract = {Two local methods for observed-score equating are applied to the problem of equating an adaptive test to a linear test. In an empirical study, the methods were evaluated against a method based on the test characteristic function (TCF) of the linear test and traditional equipercentile equating applied to the ability estimates on the adaptive test for a population of test takers. The two local methods were generally best. Surprisingly, the TCF method performed slightly worse than the equipercentile method. Both methods showed strong bias and uniformly large inaccuracy, but the TCF method suffered from extra error due to the lower asymptote of the test characteristic function. It is argued that the worse performances of the two methods are a consequence of the fact that they use a single equating transformation for an entire population of test takers and therefore have to compromise between the individual score distributions. },
	keywords = {computerized adaptive testing, equipercentile equating, local equating, score reporting, test characteristic function},
	isbn = {0146-6216 (Print)},
	author = {van der Linden, W. J.}
}
@article {398,
	title = {A comparison of item-selection methods for adaptive tests with content constraints},
	journal = {Journal of Educational Measurement},
	volume = {42},
	number = {3},
	year = {2005},
	pages = {283-302},
	publisher = {Blackwell Publishing: United Kingdom},
	abstract = {In test assembly, a fundamental difference exists between algorithms that select a test sequentially or simultaneously. Sequential assembly allows us to optimize an objective function at the examinee{\textquoteright}s ability estimate, such as the test information function in computerized adaptive testing. But it leads to the non-trivial problem of how to realize a set of content constraints on the test{\textemdash}a problem more naturally solved by a simultaneous item-selection method. Three main item-selection methods in adaptive testing offer solutions to this dilemma. The spiraling method moves item selection across categories of items in the pool proportionally to the numbers needed from them. Item selection by the weighted-deviations method (WDM) and the shadow test approach (STA) is based on projections of the future consequences of selecting an item. These two methods differ in that the former calculates a projection of a weighted sum of the attributes of the eventual test and the latter a projection of the test itself. The pros and cons of these methods are analyzed. An empirical comparison between the WDM and STA was conducted for an adaptive version of the Law School Admission Test (LSAT), which showed equally good item-exposure rates but violations of some of the constraints and larger bias and inaccuracy of the ability estimator for the WDM.},
	keywords = {Adaptive Testing, Algorithms, content constraints, item selection method, shadow test approach, spiraling method, weighted deviations method},
	isbn = {0022-0655 (Print)},
	author = {van der Linden, W. J.}
}
@article {408,
	title = {Constraining item exposure in computerized adaptive testing with shadow tests},
	journal = {Journal of Educational and Behavioral Statistics},
	volume = {29},
	number = {3},
	year = {2004},
	pages = {273-291},
	publisher = {American Educational Research Assn: US},
	abstract = {Item-exposure control in computerized adaptive testing is implemented by imposing item-ineligibility constraints on the assembly process of the shadow tests. The method resembles Sympson and Hetter{\textquoteright}s (1985) method of item-exposure control in that the decisions to impose the constraints are probabilistic. The method does not, however, require time-consuming simulation studies to set values for control parameters before the operational use of the test. Instead, it can set the probabilities of item ineligibility adaptively during the test using the actual item-exposure rates. An empirical study using an item pool from the Law School Admission Test showed that application of the method yielded perfect control of the item-exposure rates and had negligible impact on the bias and mean-squared error functions of the ability estimator. },
	keywords = {computerized adaptive testing, item exposure control, item ineligibility constraints, Probability, shadow tests},
	isbn = {1076-9986 (Print)},
	author = {van der Linden, W. J. and Veldkamp, B. P.}
}
@article {418,
	title = {Mathematical-programming approaches to test item pool design},
	number = {RR 02-09},
	year = {2002},
	note = {Using Smart Source ParsingAdvances in psychology research, Vol. ( Hauppauge, NY : Nova Science Publishers, Inc, [URL:http://www.Novapublishers.com]. vi, 228 pp},
	pages = {93-108},
	institution = {University of Twente, Faculty of Educational Science and Technology},
	address = {Twente, The Netherlands},
	abstract = {(From the chapter) This paper presents an approach to item pool design that has the potential to improve on the quality of current item pools in educational and psychological testing and hence to increase both measurement precision and validity. The approach consists of the application of mathematical programming techniques to calculate optimal blueprints for item pools. These blueprints can be used to guide the item-writing process. Three different types of design problems are discussed, namely for item pools for linear tests, item pools computerized adaptive testing (CAT), and systems of rotating item pools for CAT. The paper concludes with an empirical example of the problem of designing a system of rotating item pools for CAT.},
	keywords = {Adaptive Testing, Computer Assisted, Computer Programming, Educational Measurement, Item Response Theory, Mathematics, Psychometrics, Statistical Rotation computerized adaptive testing, Test Items, Testing},
	isbn = {02-09},
	author = {Veldkamp, B. P. and van der Linden, W. J. and Ariel, A.}
}
