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A Comparison of the Efficiency and Accuracy of BILOG and LOGIST

Published online by Cambridge University Press:  01 January 2025

Wendy M. Yen
Wendy M. Yen*
Affiliation:
CTB/McGraw-Hill
*
Requests for reprints should be sent to Wendy M. Yen, CTB/McGraw-Hill, 2500 Garden Road, Monterey, CA 93940.
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Abstract

Comparisons are made between BILOG version 2.2 and LOGIST 5.0 Version 2.5 in estimating the item parameters, traits, item characteristic functions (ICFs), and test characteristic functions (TCFs) for the three-parameter logistic model. Data analyzed are simulated item responses for 1000 simulees and one 10-item test, four 20-item tests, and four 40-item tests. LOGIST usually was faster than BILOG in producing maximum likelihood estimates. BILOG almost always produced more accurate estimates of individual item parameters. In estimating ICFs and TCFs BILOG was more accurate for the 10-item test, and the two programs were about equally accurate for the 20- and 40-item tests.

Keywords

BILOGcomputer programitem response theoryLOGISTtests

Information

Type
Computational Psychometrics
Information
Psychometrika , Volume 52 , Issue 2 , March 1987 , pp. 275 - 291
Copyright
Copyright © 1987 The Psychometric Society

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Footnotes

I am grateful to Robert J. Mislevy, Martha L. Stocking, and Marilyn S. Wingersky for many helpful comments on an earlier version of this paper. I would also like to thank Hamid Kamrani and Bongmyoung Park for getting LOGIST and BILOG running and keeping them running under changing computer systems at CTB/McGraw-Hill.

References

Bock, R. D., Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443–459.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Lord, F. M. (1969). Estimating true-score distributions in psychological testing (An empirical Bayes estimation problem). Psychometrika, 34, 259–299.CrossRefGoogle Scholar
Lord, F. M. (1974). Estimation of latent ability and item parameters when there are omitted responses. Psychometrika, 39, 247–264.CrossRefGoogle Scholar
Lord, F. M. (1975). The “ability” scale in item characteristic curve theory. Psychometrika, 40, 205–217.CrossRefGoogle Scholar
Lord, F. M. (1983). Statistical bias in maximum likelihood estimators of item parameters. Psychometrika, 48, 425–435.CrossRefGoogle Scholar
Lord, F. M. (1983). Unbiased estimators of ability parameters, of their variance, and of their parallel-forms reliability. Psychometrika, 48, 233–245.CrossRefGoogle Scholar
Lord, F. M. (1984). Maximum likelihood and Bayesian parameter estimates in item response theory, Princeton, NJ: Educational Testing Service.Google Scholar
Mislevy, R. J. (1984). Estimating latent distributions. Psychometrika, 49, 359–381.CrossRefGoogle Scholar
Mislevy, R. J., Bock, R. D. (1984). BILOG Version 2.2.: Item analysis and test scoring with binary logistic models, Mooresville, IN: Scientific Software.Google Scholar
Qualls, A. L., & Ansley, T. N. (1985, April). A comparison of item and ability parameter estimates derived from LOGIST and BILOG. Paper presented at the meeting of the National Council on Measurement in Education, Chicago, IL.Google Scholar
Stocking, M. L., Lord, F. M. (1983). Developing a common metric in item response theory. Applied Psychological Measurement, 7, 201–210.CrossRefGoogle Scholar
Wingersky, M. S. (1983). LOGIST: A program for computing maximum likelihood procedures for logistic test models. In Hambleton, R. K. (Eds.), Applications of item response theory (pp. 45–56). British Columbia: Educational Research Institute of British Columbia.Google Scholar
Wingersky, M. S., Barton, M. A., Lord, F. M. (1982). LOGIST 5.0 version 1.0 users' guide, Princeton, NJ: Educational Testing Service.Google Scholar
Wingersky, M. S., Lord, F. M. (1973). A computer program for estimating examinee ability and item characteristic curve parameters when there are omitted responses, Princeton, NJ: Educational Testing Service.Google Scholar
Yen, W. M. (1983). Tau equivalence and equipercentile equating. Psychometrika, 48, 353–369.CrossRefGoogle Scholar
Yen, W. M. (1984). Obtaining maximum likelihood trait estimates from number-correct scores for the three-parameter logistic model. Journal of Educational Measurement, 21, 93–111.CrossRefGoogle Scholar
Yen, W. M. (1985). Increasing item complexity: A possible cause of scale shrinkage for unidimensional item response theory. Psychometrika, 50, 399–410.CrossRefGoogle Scholar