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Equipercentile Equating Via Data-Imputation Techniques

Published online by Cambridge University Press:  01 January 2025

Michelle Liou  and
Philip E. Cheng
Michelle Liou*
Affiliation:
Academia Sinica
Philip E. Cheng
Affiliation:
Academia Sinica
*
Requests for reprints should be sent to Michelle Liou, Educational Testing Service, LARG—Mail Stop 02-T, Rosedale Road, Princeton, NJ 08541.
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Abstract

In the design of common-item equating, two groups of examinees are administered separate test forms, and each test form contains a common subset of items. We consider test equating under this situation as an incomplete data problem—that is, examinees have observed scores on one test form and missing scores on the other. Through the use of statistical data-imputation techniques, the missing scores can be replaced by reasonable estimates, and consequently the forms may be directly equated as if both forms were administered to both groups. In this paper we discuss different data-imputation techniques that are useful for equipercentile equating; we also use empirical data to evaluate the accuracy of these techniques as compared with chained equipercentile equating.

Keywords

extended beta-binomial modelcommon-item equatingEM algorithmequipercentile equatinglog-linear smoothingmoment estimator
Type
Original Paper
Information
Psychometrika , Volume 60 , Issue 1 , March 1995 , pp. 119 - 136
Copyright
Copyright © 1995 The Psychometric Society

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Footnotes

A paper presented at the European Meeting of the Psychometric Society, Barcelona, Spain, July, 1993.

We would like to thank Heng-Syung Jeng and the College Entrance Examination Center, ROC, for providing the empirical data used in this research, to Chang-Yung Yu for writing the programs, and to the Editor and referees for their valuable comments on drafts of our manuscript.

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