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Online Calibration Via Variable Length Computerized Adaptive Testing

Published online by Cambridge University Press:  01 January 2025

Yuan-chin Ivan Chang  and
Hung-Yi Lu
Yuan-chin Ivan Chang*
Affiliation:
Academia Sinica
Hung-Yi Lu
Affiliation:
Fu Jen Catholic University
*
Requests for reprints should be sent to Yuan-chin Ivan Chang, Academia Sinica, Taipei, Taiwan. E-mail: ycchang@sinica.edu.tw
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Abstract

Item calibration is an essential issue in modern item response theory based psychological or educational testing. Due to the popularity of computerized adaptive testing, methods to efficiently calibrate new items have become more important than that in the time when paper and pencil test administration is the norm. There are many calibration processes being proposed and discussed from both theoretical and practical perspectives. Among them, the online calibration may be one of the most cost effective processes. In this paper, under a variable length computerized adaptive testing scenario, we integrate the methods of adaptive design, sequential estimation, and measurement error models to solve online item calibration problems. The proposed sequential estimate of item parameters is shown to be strongly consistent and asymptotically normally distributed with a prechosen accuracy. Numerical results show that the proposed method is very promising in terms of both estimation accuracy and efficiency. The results of using calibrated items to estimate the latent trait levels are also reported.

Keywords

adaptive testingitem response theorylogistic regressionmeasurement errornonlinear designonline calibrationstopping rule
Type
Theory and Methods
Information
Psychometrika , Volume 75 , Issue 1 , March 2010 , pp. 140 - 157
Copyright
Copyright © 2010 The Psychometric Society

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