@article {181,
	title = {Optimal and nonoptimal computer-based test designs for making pass-fail decisions},
	journal = {Applied Measurement in Education},
	volume = {19},
	number = {3},
	year = {2006},
	pages = {221-239},
	publisher = {Lawrence Erlbaum: US},
	abstract = {Now that many credentialing exams are being routinely administered by computer, new computer-based test designs, along with item response theory models, are being aggressively researched to identify specific designs that can increase the decision consistency and accuracy of pass-fail decisions. The purpose of this study was to investigate the impact of optimal and nonoptimal multistage test (MST) designs, linear parallel-form test designs (LPFT), and computer adaptive test (CAT) designs on the decision consistency and accuracy of pass-fail decisions. Realistic testing situations matching those of one of the large credentialing agencies were simulated to increase the generalizability of the findings. The conclusions were clear: (a) With the LPFTs, matching test information functions (TIFs) to the mean of the proficiency distribution produced slightly better results than matching them to the passing score; (b) all of the test designs worked better than test construction using random selection of items, subject to content constraints only; (c) CAT performed better than the other test designs; and (d) if matching a TIP to the passing score, the MST design produced a bit better results than the LPFT design. If an argument for the MST design is to be made, it can be made on the basis of slight improvements over the LPFT design and better expected item bank utilization, candidate preference, and the potential for improved diagnostic feedback, compared with the feedback that is possible with fixed linear test forms. (PsycINFO Database Record (c) 2007 APA, all rights reserved)},
	keywords = {adaptive test, credentialing exams, Decision Making, Educational Measurement, multistage tests, optimal computer-based test designs, test form},
	isbn = {0895-7347 (Print); 1532-4818 (Electronic)},
	author = {Hambleton, R. K. and Xing, D.}
}
@booklet {1393,
	title = {Computer-based test designs with optimal and non-optimal tests for making pass-fail decisions},
	year = {2004},
	address = {Research Report, University of Massachusetts, Amherst, MA},
	author = {Hambleton, R. K. and Xing, D.}
}
@article {379,
	title = {Small sample estimation in dichotomous item response models: Effect of priors based on judgmental information on the accuracy of item parameter estimates},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {1},
	year = {2003},
	note = {Sage Publications, US},
	pages = {27-51},
	abstract = {Large item banks with properly calibrated test items are essential for ensuring the validity of computer-based tests. At the same time, item calibrations with small samples are desirable to minimize the amount of pretesting and limit item exposure. Bayesian estimation procedures show considerable promise with small examinee samples. The purposes of the study were (a) to examine how prior information for Bayesian item parameter estimation can be specified and (b) to investigate the relationship between sample size and the specification of prior information on the accuracy of item parameter estimates. The results of the simulation study were clear: Estimation of item response theory (IRT) model item parameters can be improved considerably. Improvements in the one-parameter model were modest; considerable improvements with the two- and three-parameter models were observed. Both the study of different forms of priors and ways to improve the judgmental data used in forming the priors appear to be promising directions for future research. },
	author = {Swaminathan, H. and Hambleton, R. K. and Sireci, S. G. and Xing, D. and Rizavi, S. M.}
}
@conference {1274,
	title = {Impact of test design, item quality and item bank size on the psychometric properties of computer-based credentialing exams},
	booktitle = {Paper presented at the meeting of National Council on Measurement in Education},
	year = {2002},
	note = {PDF file, 500 K},
	address = {New Orleans},
	author = {Xing, D. and Hambleton, R. K.}
}
@conference {1273,
	title = {Impact of several computer-based testing variables on the psychometric properties of credentialing examinations},
	booktitle = {Paper presented at the Annual Meeting of the National Council on Measurement in Education},
	year = {2001},
	address = {Seattle WA},
	author = {Xing, D. and Hambleton, R. K.}
}
@booklet {1582,
	title = {Impact of several computer-based testing variables on the psychometric properties of credentialing examinations (Laboratory of Psychometric and Evaluative Research Report No 393)},
	year = {2001},
	address = {Amherst, MA: University of Massachusetts, School of Education.},
	author = {Xing, D. and Hambleton, R. K.}
}
@conference {1124,
	title = {Classification accuracy and test security for a computerized adaptive mastery test calibrated with different IRT models},
	booktitle = {Paper presented at the annual meeting of the National Council on Measurement in Education},
	year = {2000},
	address = {New Orleans LA},
	author = {Robin, F. and Xing, D. and Scrams, D. and Potenza, M.}
}
