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- Publisher Website: 10.1007/s11336-020-09724-3
- Scopus: eid_2-s2.0-85091535401
- PMID: 32979182
- WOS: WOS:000572970400002
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Article: A Partially Confirmatory Approach to the Multidimensional Item Response Theory with the Bayesian Lasso
Title | A Partially Confirmatory Approach to the Multidimensional Item Response Theory with the Bayesian Lasso |
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Authors | |
Keywords | MIRT Bayesian Lasso partially confirmatory Lasso loading local dependence |
Issue Date | 2020 |
Citation | Psychometrika, 2020, v. 85 n. 3, p. 738-774 How to Cite? |
Abstract | © 2020, The Psychometric Society. 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. |
Persistent Identifier | http://hdl.handle.net/10722/288831 |
ISSN | 2023 Impact Factor: 2.9 2023 SCImago Journal Rankings: 2.376 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, Jinsong | - |
dc.date.accessioned | 2020-10-12T08:05:59Z | - |
dc.date.available | 2020-10-12T08:05:59Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Psychometrika, 2020, v. 85 n. 3, p. 738-774 | - |
dc.identifier.issn | 0033-3123 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288831 | - |
dc.description.abstract | © 2020, The Psychometric Society. 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. | - |
dc.language | eng | - |
dc.relation.ispartof | Psychometrika | - |
dc.subject | MIRT | - |
dc.subject | Bayesian Lasso | - |
dc.subject | partially confirmatory | - |
dc.subject | Lasso loading | - |
dc.subject | local dependence | - |
dc.title | A Partially Confirmatory Approach to the Multidimensional Item Response Theory with the Bayesian Lasso | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11336-020-09724-3 | - |
dc.identifier.pmid | 32979182 | - |
dc.identifier.scopus | eid_2-s2.0-85091535401 | - |
dc.identifier.hkuros | 316819 | - |
dc.identifier.volume | 85 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 738 | - |
dc.identifier.epage | 774 | - |
dc.identifier.eissn | 1860-0980 | - |
dc.identifier.isi | WOS:000572970400002 | - |
dc.identifier.issnl | 0033-3123 | - |