@article {370,
	title = {An EM approach to parameter estimation for the Zinnes and Griggs paired comparison IRT model},
	journal = {Applied Psychological Measurement},
	volume = {26},
	number = {2},
	year = {2002},
	pages = {208-227},
	abstract = {Borman et al. recently proposed a computer adaptive performance appraisal system called CARS II that utilizes paired comparison judgments of behavioral stimuli. To implement this approach,the paired comparison ideal point model developed by Zinnes and Griggs was selected. In this article,the authors describe item response and information functions for the Zinnes and Griggs model and present procedures for estimating stimulus and person parameters. Monte carlo simulations were conducted to assess the accuracy of the parameter estimation procedures. The results indicated that at least 400 ratees (i.e.,ratings) are required to obtain reasonably accurate estimates of the stimulus parameters and their standard errors. In addition,latent trait estimation improves as test length increases. The implications of these results for test construction are also discussed. },
	keywords = {Adaptive Testing, Computer Assisted Testing, Item Response Theory, Maximum Likelihood, Personnel Evaluation, Statistical Correlation, Statistical Estimation},
	author = {Stark, S. and F Drasgow}
}
@article {277,
	title = {Outlier detection in high-stakes certification testing},
	journal = {Journal of Educational Measurement},
	volume = {39},
	number = {3},
	year = {2002},
	pages = {219-233},
	abstract = {Discusses recent developments of person-fit analysis in computerized adaptive testing (CAT). Methods from statistical process control are presented that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory model in CAT Most person-fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used. Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high-stakes certification test, the empirical analysis showed that different types of misfit can be distinguished Further applications using statistical process control methods to detect misfitting item score patterns are discussed. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, computerized adaptive testing, Educational Measurement, Goodness of Fit, Item Analysis (Statistical), Item Response Theory, person Fit, Statistical Estimation, Statistical Power, Test Scores},
	author = {Meijer, R. R.}
}
@article {336,
	title = {Computerized adaptive testing with the generalized graded unfolding model},
	journal = {Applied Psychological Measurement},
	volume = {25},
	number = {2},
	year = {2001},
	pages = {177-196},
	abstract = {Examined the use of the generalized graded unfolding model (GGUM) in computerized adaptive testing. The objective was to minimize the number of items required to produce equiprecise estimates of person locations. Simulations based on real data about college student attitudes toward abortion and on data generated to fit the GGUM were used. It was found that as few as 7 or 8 items were needed to produce accurate and precise person estimates using an expected a posteriori procedure. The number items in the item bank (20, 40, or 60 items) and their distribution on the continuum (uniform locations or item clusters in moderately extreme locations) had only small effects on the accuracy and precision of the estimates. These results suggest that adaptive testing with the GGUM is a good method for achieving estimates with an approximately uniform level of precision using a small number of items. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Attitude Measurement, College Students computerized adaptive testing, Computer Assisted Testing, Item Response, Models, Statistical Estimation, Theory},
	author = {Roberts, J. S. and Lin, Y. and Laughlin, J. E.}
}
@inbook {385,
	title = {Item response theory applied to combinations of multiple-choice and constructed-response items--approximation methods for scale scores},
	booktitle = {Test scoring},
	year = {2001},
	note = {Using Smart Source ParsingTest scoring. (pp. 293-341). Mahwah, NJ : Lawrence Erlbaum Associates, Publishers. xii, 422 pp},
	pages = {289-315},
	publisher = {Lawrence Erlbaum Associates},
	organization = {Lawrence Erlbaum Associates},
	chapter = {8},
	address = {Mahwah, N.J. USA},
	abstract = {(From the chapter) The authors develop approximate methods that replace the scoring tables with weighted linear combinations of the component scores. Topics discussed include: a linear approximation for the extension to combinations of scores; the generalization of two or more scores; potential applications of linear approximations to item response theory in computerized adaptive tests; and evaluation of the pattern-of-summed-scores, and Gaussian approximation, estimates of proficiency. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, Item Response Theory, Method), Multiple Choice (Testing, Scoring (Testing), Statistical Estimation, Statistical Weighting, Test Items, Test Scores},
	author = {Thissen, D. and Nelson, L. A. and Swygert, K. A.}
}
