@article {390,
	title = {A framework for the development of computerized adaptive tests},
	journal = {Practical Assessment Research \& Evaluation},
	volume = {16},
	number = {1},
	year = {2011},
	publisher = {Practical Assessment Research \& Evaluation},
	abstract = {A substantial amount of research has been conducted over the past 40 years on technical aspects of computerized adaptive testing (CAT), such as item selection algorithms, item exposure controls, and termination criteria. However, there is little literature providing practical guidance on the development of a CAT. This paper seeks to collate some of the available research methodologies into a general framework for the development of any CAT assessment. },
	author = {Thompson, N. A. and Weiss,  D. J.}
}
@article {2037,
	title = {JATT Special Issue on Adaptive Testing: Welcome and Overview},
	journal = {Journal of Applied Testing Technology},
	volume = {12},
	year = {2011},
	month = {05/2011},
	url = {http://www.testpublishers.org/journal-of-applied-testing-technology},
	author = {Thompson, N. A.}
}
@article {388,
	title = {Item selection in computerized classification testing},
	journal = {Educational and Psychological Measurement},
	volume = {69},
	number = {5},
	year = {2009},
	pages = {778-793},
	abstract = {Several alternatives for item selection algorithms based on item response theory in computerized classification testing (CCT) have been suggested, with no conclusive evidence on the substantial superiority of a single method. It is argued that the lack of sizable effect is because some of the methods actually assess items very similarly through different calculations and will usually select the same item. Consideration of methods that assess information across a wider range is often unnecessary under realistic conditions, although it might be advantageous to utilize them only early in a test. In addition, the efficiency of item selection approaches depend on the termination criteria that are used, which is demonstrated through didactic example and Monte Carlo simulation. Item selection at the cut score, which seems conceptually appropriate for CCT, is not always the most efficient option. A broad framework for item selection in CCT is presented that incorporates these points. },
	isbn = {0013-1644},
	author = {Thompson, N. A.}
}
@inbook {1911,
	title = {The MEDPRO project: An SBIR project for a comprehensive IRT and CAT software system: CAT software},
	year = {2009},
	note = {{PDF File, 283 KB}},
	address = {D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.},
	abstract = {Development of computerized adaptive tests (CAT) requires a number of appropriate software tools.  This paper describes the development of two new CAT software programs.  CATSIM has been designed specifically to conduct several different kinds of simulation studies, which are necessary for planning purposes as well as properly designing live CATs.  FastCAT is a software system for banking items and publishing CAT tests as standalone files, to be administered anywhere.  Both are available for public use.},
	author = {Thompson, N. A.}
}
@inbook {1912,
	title = {Utilizing the generalized likelihood ratio as a termination criterion},
	year = {2009},
	note = {{PDF File, 194 KB}},
	address = {D. J. Weiss (Ed.), Proceedings of the 2009 GMAC Conference on Computerized Adaptive Testing.},
	abstract = {Computer-based testing can be used to classify examinees into mutually exclusive groups.  Currently, the predominant psychometric algorithm for designing computerized classification tests (CCTs) is the sequential probability ratio test (SPRT; Reckase, 1983) based on item response theory (IRT).  The SPRT has been shown to be more efficient than confidence intervals around θ estimates as a method for CCT delivery (Spray \& Reckase, 1996; Rudner, 2002).  More recently, it was demonstrated that the SPRT, which only uses fixed values, is less efficient than a generalized form which tests whether a given examinee{\textquoteright}s θ is below θ1or above θ2 (Thompson, 2007).  This formulation allows the indifference region to vary based on observed data.  Moreover, this composite hypothesis formulation better represents the conceptual purpose of the test, which is to test whether θ is above or below the cutscore.  The purpose of this study was to explore the specifications of the new generalized likelihood ratio (GLR; Huang, 2004).  As with the SPRT, the efficiency of the procedure depends on the nominal error rates and the distance between θ1 and θ2 (Eggen, 1999).  This study utilized a monte-carlo approach, with 10,000 examinees simulated under each condition, to evaluate differences in efficiency and accuracy due to hypothesis structure, nominal error rate, and indifference region size.  The GLR was always at least as efficient as the fixed-point SPRT while maintaining equivalent levels of accuracy.  },
	author = {Thompson, N. A.}
}
@book {1703,
	title = {A comparison of two methods of polytomous computerized classification testing for multiple cutscores},
	year = {2007},
	note = {{PDF file, 363 KB}},
	address = {Unpublished doctoral dissertation, University of Minnesota},
	author = {Thompson, N. A.}
}
@proceedings {389,
	title = {Computerized classification testing with composite hypotheses},
	journal = {GMAC Conference on Computerized Adaptive Testing},
	year = {2007},
	note = {Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing. Retrieved [date] from www. psych. umn. edu/psylabs/CATCentral},
	publisher = {Graduate Management Admissions Council},
	address = {St. Paul, MN},
	keywords = {computerized adaptive testing},
	author = {Thompson, N. A. and Ro, S.}
}
@inbook {1913,
	title = {Computerized classification testing with composite hypotheses},
	year = {2007},
	note = {{PDF file, 96 KB}},
	address = {D. J. Weiss (Ed.). Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.},
	author = {Thompson, N. A. and Ro, S.}
}
@conference {1122,
	title = {Cutscore location and classification accuracy in computerized classification testing},
	booktitle = {Paper presented at the international meeting of the Psychometric Society},
	year = {2007},
	note = {{PDF file, 94 KB}},
	address = {Tokyo, Japan},
	author = {Ro, S. and Thompson, N. A.}
}
@conference {1202,
	title = {Item selection in computerized classification testing},
	booktitle = {Paper presented at the Conference on High Stakes Testing},
	year = {2007},
	note = {{PDF file, 87KB}},
	address = {University of Nebraska},
	author = {Thompson, N. A.}
}
@article {2178,
	title = {A Practitioner{\textquoteright}s Guide for Variable-length Computerized Classification Testing},
	journal = {Practical Assessment Research and Evaluation},
	volume = {12},
	year = {2007},
	pages = {1-13},
	author = {Thompson, N. A.}
}
@article {387,
	title = {A practitioner{\textquoteright}s guide to variable-length computerized classification testing},
	journal = {Practical Assessment, Research and Evaluation},
	volume = {12 },
	number = {1},
	year = {2007},
	month = {7/1/2009},
	chapter = {January, 2007},
	abstract = {Variable-length computerized classification tests, CCTs, (Lin \& Spray, 2000; Thompson, 2006) are a powerful and efficient approach to testing for the purpose of classifying examinees into groups. CCTs are designed by the specification of at least five technical components: psychometric model, calibrated item bank, starting point, item selection algorithm, and termination criterion. Several options exist for each of these CCT components, creating a myriad of possible designs. Confusion among designs is exacerbated by the lack of a standardized nomenclature. This article outlines the components of a CCT, common options for each component, and the interaction of options for different components, so that practitioners may more efficiently design CCTs. It also offers a suggestion of nomenclature. },
	keywords = {CAT, classification, computer adaptive testing, computerized adaptive testing, Computerized classification testing},
	author = {Thompson, N. A.}
}
