This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Wang, T. Articles by Lau, C.-M. A. Search for Related Content Social Bookmarking                         What's this?

Reducing Bias in CAT Trait Estimation: A Comparison of Approaches

wang{at}act.org

Bradley A. Hanson

ACT, Inc.

Che-Ming A. Lau

Harcourt Brace Educational Measurement

The use of a beta prior in trait estimation was extended to the maximum expected a posteriori (MAP) method of Bayesian estimation. This new method, called essentially unbiased MAP (EU-MAP), was compared with MAP (using a standard normal prior), essentially unbiased expected a posteriori, weighted likelihood, and maximum likelihood estimation methods. Comparisons were made based on the effects that the shape of prior distributions, different item bank characteristics, and practical constraints had on bias, standard error, and root-mean-square error (RMSE). Overall, EU-MAP performed best. This new method significantly reduced bias in fixed-length tests (though with a slight increase in RMSE) and performed reasonably well when a fixed posterior variance termination rule was used. Practical constraints had little effect on the bias of this method.

Applied Psychological Measurement, Vol. 23, No. 3, 263-278 (1999)
DOI: 10.1177/01466219922031383


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				V. L. Passos, M. P. F. Berger, and F. E. S. Tan The D-Optimality Item Selection Criterion in the Early Stage of CAT: A Study With the Graded Response Model Journal of Educational and Behavioral Statistics, March 1, 2008; 33(1): 88 - 110. [Abstract] [Full Text] [PDF]

				R. D. Penfield Estimating the Standard Error of the Maximum Likelihood Ability Estimator in Adaptive Testing Using the Posterior-Weighted Test Information Function Educational and Psychological Measurement, December 1, 2007; 67(6): 958 - 975. [Abstract] [PDF]

				V. Lima Passos, M. P. F. Berger, and F. E. Tan Test Design Optimization in CAT Early Stage with the Nominal Response Model Applied Psychological Measurement, May 1, 2007; 31(3): 213 - 232. [Abstract] [PDF]

				R. D. Penfield and J. M. Bergeron Applying a Weighted Maximum Likelihood Latent Trait Estimator to the Generalized Partial Credit Model Applied Psychological Measurement, May 1, 2005; 29(3): 218 - 233. [Abstract] [PDF]

				W.-C. Wang and P.-H. Chen Implementation and Measurement Efficiency of Multidimensional Computerized Adaptive Testing Applied Psychological Measurement, September 1, 2004; 28(5): 295 - 316. [Abstract] [PDF]

				S. Wang and T. Wang Precision of Warm's Weighted Likelihood Estimates for a Polytomous Model in Computerized Adaptive Testing Applied Psychological Measurement, December 1, 2001; 25(4): 317 - 331. [Abstract] [PDF]