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A Partially Confirmatory Approach to the Multidimensional Item Response Theory with the Bayesian Lasso

Published online by Cambridge University Press:  01 January 2025

Jinsong Chen Open the ORCID record for Jinsong Chen [Opens in a new window]
Jinsong Chen*
Affiliation:
The University of Hong Kong
*
Correspondence should be made to Jinsong Chen, Faculty of Education, The University of Hong Kong, Room 420, 4/F, Meng Wah Complex, Pokfulam Road, Hong Kong, China. Email: [email protected]
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Abstract

For test development in the setting of multidimensional item response theory, the exploratory and confirmatory approaches lie on two ends of a continuum in terms of the loading and residual structures. Inspired by the recent development of the Bayesian Lasso (least absolute shrinkage and selection operator), this research proposes a partially confirmatory approach to estimate both structures using Bayesian regression and a covariance Lasso within a unified framework. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation, and the shrinkage parameters are estimated simultaneously. The proposed approach with different model variants and constraints was found to be flexible in addressing loading selection and local dependence. Both simulated and real-life data were analyzed to evaluate the performance of the proposed model across different situations.

Keywords

MIRTBayesian Lassopartially confirmatoryLasso loadinglocal dependence
Type
Theory and Methods
Information
Psychometrika , Volume 85 , Issue 3 , September 2020 , pp. 738 - 774
Copyright
Copyright © 2020 The Psychometric Society

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