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Additive Multilevel Item Structure Models with Random Residuals: Item Modeling for Explanation and Item Generation

Published online by Cambridge University Press:  01 January 2025

Sun-Joo Cho ,
Paul De Boeck ,
Susan Embretson  and
Sophia Rabe-Hesketh
Sun-Joo Cho*
Affiliation:
Vanderbilt University
Paul De Boeck
Affiliation:
Ohio State University and Ku Leuven
Susan Embretson
Affiliation:
Georgia Institute of Technology
Sophia Rabe-Hesketh
Affiliation:
University of California, Berkeley and Institute of Education, University of London
*
Requests for reprints should be sent to Sun-Joo Cho, Vanderbilt University, Nashville, USA. E-mail: [email protected]
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Abstract

An additive multilevel item structure (AMIS) model with random residuals is proposed. The model includes multilevel latent regressions of item discrimination and item difficulty parameters on covariates at both item and item category levels with random residuals at both levels. The AMIS model is useful for explanation purposes and also for prediction purposes as in an item generation context. The parameters can be estimated with an alternating imputation posterior algorithm that makes use of adaptive quadrature, and the performance of this algorithm is evaluated in a simulation study.

Keywords

alternating imputation posterior with adaptive quadratureitem generationmultilevel modelrandom item parameters
Type
Original Paper
Information
Psychometrika , Volume 79 , Issue 1 , January 2014 , pp. 84 - 104
Copyright
Copyright © 2013 The Psychometric Society

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