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Abstract

This study compared pseudo-Bayesian and joint maximum likelihood procedures for estimating item parameters for the three-parameter logistic model in item response theory. Two programs, ASCAL and LOGIST, which employ the two methods were com pared using data simulated from a three-parameter model. Item responses were generated for sample sizes of 2,000 and 500, test lengths of 35 and 15, and examin ees of high, medium, and low ability. The results showed that the item characteristic curves estimated by the two methods were more similar to each other than to the generated item characteristic curves. Pseudo- Bayesian estimation consistently produced more accu rate item parameter estimates for the smaller sample size, whereas joint maximum likelihood was more ac curate as test length was reduced. Index terms: ASCAL, item response theory, joint maximum likelihood estimation, LOGIST, parameter estimation, pseudo- Bayesian estimation, three-parameter model.

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References

Assessment Systems Corporation. (1986). MICROCAT: A computer program for computerized adaptive testing. St. Paul: Author.

Baker, F.B. (1983). Comparison of ability metrics obtained under two latent trait theory procedures. Applied Psychological Measurement , 7, 97-110.

Baker, F.B. (1987). Methodology review: Item parameter estimation under the one-, two-, and three-parameter logistic models. Applied Psychological Measurement, 11, 111-141.

Bimbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick, Statistical theories of mental test scores (pp. 397-479). Reading MA: Addison-Wesley.

Hulin, C.L. , Lissak, R.I. , & Drasgow, F. (1982). Recovery of two- and three-parameter logistic item characteristic curves: A monte carlo study. Applied Psychological Measurement, 6, 249-260.

Imsl, Inc. (1980). International mathematical and statistical libraries reference manual. Houston: Author .

Jensema, C.J. (1976). A simple technique for estimating latent trait mental test parameters. Educational and Psychological Measurement , 36, 705-715.

Lord, F.M. (1984). Maximum likelihood and Bayesian parameter estimation in item response theory (ETS Technical Report No. RR-84-30-ONR). Princeton NJ: Educational Testing Service.

Mislevy, R.J. , & Bock, R.D. (1982). BILOG: Maximum likelihood item analysis and test scoring with logistic models for binary items. Chicago : International Educational Services.

10.

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research. [Expanded edition, University of Chicago Press, 1980.]

11.

Stocking, M.L. , & Lord, F.M. (1983). Developing a common metric in item response theory . Applied Psychological Measurement, 7, 201-210.

12.

Swaminathan, H. , & Gifford, J.A. (1983). Estimation of parameters in the three-parameter latent trait model. In D. J. Weiss (Ed.), New horizons in testing: Latent trait test theory and computerized adaptive testing (pp. 13-30). New York: Academic Press.

13.

Swaminathan, H. , & Gifford, J.A. (1985). Bayesian estimation in the two-parameter logistic model. Psychometrika, 50, 349-364.

14.

Vale, C.D. , & Gialluca, K.A. (1985). ASCAL: A microcomputer program for estimating logistic IRT item parameters (ONR-85-4). St. Paul: Assessment Systems Corporation.

15.

Vale, C.D. , & Gialluca, K.A. (1988). Evaluation of the efficiency of item calibration. Applied Psychological Measurement, 12, 53-67.

16.

Wingersky, M.S. , Barton, M.H. , & Lord, F.M. (1983). LOGIST: A computer program for estimating examinee ability and item characteristic curve parameters. LOGIST 5, Version 1. Princeton NJ: Educational Testing Service.

A Comparison of Pseudo-Bayesian and Joint Maximum Likelihood Procedures for Estimating Item Parameters in the Three-Parameter IRT Model

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