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A Note on Item Information in Any Direction for the Multidimensional Three-Parameter Logistic Model

Published online by Cambridge University Press:  01 January 2025

Damon U. Bryant
Damon U. Bryant*
Affiliation:
University Of Central FLorida at Orlando
*
Requests for reprints should be sent to Damon U. Bryant, University of Central Florida, Department of Psychology, PH 215 Orlando, FL 32816-1390, USA. E-mail: dbryant@ucf.edu, Tel.: +1-321-4396733
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Abstract

The purpose of this note is twofold: (a) to present the formula for the item information function (IIF) in any direction for the Multidimensional 3-Parameter Logistic (M3-PL) model and (b) to give the equation for the location of maximum item information (θmax) in the direction of the item discrimination vector. Several corollaries are given. Implications for future research are discussed.

Keywords

item informationmeasurement directionmultidimensional measurementmaximum informationthree-parameter logistic model

Information

Type
Original Paper
Information
Psychometrika , Volume 70 , Issue 1 , March 2005 , pp. 213 - 216
Copyright
Copyright © 2005 The Psychometric Society

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Footnotes

This research was supported in part by an Educational Testing Service (ETS) Harold T. Gulliksen Psychometric Research Fellowship to the author.

References

Ackerman, T.A., & Evans, J.A. (1994). The influence of conditioning scores in performing DIF analyses. Applied Psychological Measurement, 18, 329–342.CrossRefGoogle Scholar
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In Lord, F.M., & Novick, M.R. (Eds.), Statistical Theories of Mental Test Scores (pp. 453–479). Reading, MA: Addison-Wesley.Google Scholar
Hambleton, R.K., & Swaminathan, H. (1985). Item Response Theory: Principles and Applications. Kluwer Academic Publishers: Norwell, MA.CrossRefGoogle Scholar
Reckase, M.D. (1985). The difficulty of test items that measure more than one ability. Applied Psychological Measurement, 9, 401–412.CrossRefGoogle Scholar
Reckase, M.D. (1997). A linear logistic multidimensional model for dichotomous item response data. In van der Linden, W.J., & Hambleton, R. K. (Eds.), Handbook of Modern Item Response Theory (pp. 271–286). New York: Springer-Verlag.CrossRefGoogle Scholar
Reckase, M.D., & McKinley, R.L. (1991). The discriminating power of items that measure more than one dimension. Applied Psychological Measurement, 15, 361–373.CrossRefGoogle Scholar
Samejima, F. (1977). A use of the information function in tailored testing. Applied Psychological Measurement, 1, 233–247.CrossRefGoogle Scholar
Segall, D.O. (1996). Multidimensional adaptive testing. Psychometrika, 61, 331–354.CrossRefGoogle Scholar
van der Linden, W.J. (1999). Multidimensional adaptive testing with a minimum error-variance criterion. Journal of Educational and Behavioral Statistics, 24, 398–412.CrossRefGoogle Scholar
Weiss, D.J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6, 473–492.CrossRefGoogle Scholar