@article {14,
	title = {Data sparseness and on-line pretest item calibration-scaling methods in CAT},
	journal = {Journal of Educational Measurement},
	volume = {39},
	number = {3},
	year = {2002},
	pages = {207-218},
	abstract = {Compared and evaluated 3 on-line pretest item calibration-scaling methods (the marginal maximum likelihood estimate with 1 expectation maximization [EM] cycle [OEM] method, the marginal maximum likelihood estimate with multiple EM cycles [MEM] method, and M. L. Stocking{\textquoteright}s Method B) in terms of item parameter recovery when the item responses to the pretest items in the pool are sparse. Simulations of computerized adaptive tests were used to evaluate the results yielded by the three methods. The MEM method produced the smallest average total error in parameter estimation, and the OEM method yielded the largest total error (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Computer Assisted Testing, Educational Measurement, Item Response Theory, Maximum Likelihood, Methodology, Scaling (Testing), Statistical Data},
	author = {Ban, J-C. and Hanson, B. A. and Yi, Q. and Harris, D. J.}
}
@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}
}
