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Modeling Rule-Based Item Generation

Published online by Cambridge University Press:  01 January 2025

Hanneke Geerlings ,
Cees A. W. Glas  and
Wim J. van der Linden
Hanneke Geerlings*
Affiliation:
University of Twente
Cees A. W. Glas
Affiliation:
University of Twente
Wim J. van der Linden
Affiliation:
CTB/McGraw-Hill
*
Requests for reprints should be sent to Hanneke Geerlings, Department of Research Methodology, Measurement, and Data Analysis, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands. E-mail: h.geerlings@gw.utwente.nl
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Abstract

An application of a hierarchical IRT model for items in families generated through the application of different combinations of design rules is discussed. Within the families, the items are assumed to differ only in surface features. The parameters of the model are estimated in a Bayesian framework, using a data-augmented Gibbs sampler. An obvious application of the model is computerized algorithmic item generation. Such algorithms have the potential to increase the cost-effectiveness of item generation as well as the flexibility of item administration. The model is applied to data from a non-verbal intelligence test created using design rules. In addition, results from a simulation study conducted to evaluate parameter recovery are presented.

Keywords

hierarchical modelingitem generationitem response theoryMarkov chain Monte Carlo method

Information

Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 2 , April 2011 , pp. 337 - 359
Copyright
Copyright © 2011 The Psychometric Society

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