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A Gibbs‐INLA algorithm for multidimensional graded response model analysis.

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Title: A Gibbs‐INLA algorithm for multidimensional graded response model analysis.
Authors: Lin, Xiaofan (AUTHOR), Zhang, Siliang (AUTHOR), Tang, Yincai (AUTHOR), Li, Xuan (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. Feb2024, Vol. 77 Issue 1, p169-195. 27p.
Subjects: Multidimensional databases, Markov chain Monte Carlo, Gibbs sampling, Item response theory, Algorithms
Abstract: In this paper, we propose a novel Gibbs‐INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning which is inevitable in classical Markov chain Monte Carlo (MCMC) algorithm, and has low computing memory, high computational efficiency with much fewer iterations, and still achieve higher estimation accuracy. Therefore, it has the ability to handle large amount of multidimensional response data with different item responses. Simulation studies are conducted to compare with the Metroplis‐Hastings Robbins‐Monro (MH‐RM) algorithm and an application to the study of the IPIP‐NEO personality inventory data is given to assess the performance of the new algorithm. Extensions of the proposed algorithm for application on more complicated models and different data types are also discussed. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: A Gibbs‐INLA algorithm for multidimensional graded response model analysis.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Lin%2C+Xiaofan%22">Lin, Xiaofan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Siliang%22">Zhang, Siliang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Yincai%22">Tang, Yincai</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Xuan%22">Li, Xuan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. Feb2024, Vol. 77 Issue 1, p169-195. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Multidimensional+databases%22">Multidimensional databases</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Gibbs+sampling%22">Gibbs sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this paper, we propose a novel Gibbs‐INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning which is inevitable in classical Markov chain Monte Carlo (MCMC) algorithm, and has low computing memory, high computational efficiency with much fewer iterations, and still achieve higher estimation accuracy. Therefore, it has the ability to handle large amount of multidimensional response data with different item responses. Simulation studies are conducted to compare with the Metroplis‐Hastings Robbins‐Monro (MH‐RM) algorithm and an application to the study of the IPIP‐NEO personality inventory data is given to assess the performance of the new algorithm. Extensions of the proposed algorithm for application on more complicated models and different data types are also discussed. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/bmsp.12321
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      – Code: eng
        Text: English
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        PageCount: 27
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      – SubjectFull: Multidimensional databases
        Type: general
      – SubjectFull: Markov chain Monte Carlo
        Type: general
      – SubjectFull: Gibbs sampling
        Type: general
      – SubjectFull: Item response theory
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      – SubjectFull: Algorithms
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              M: 02
              Text: Feb2024
              Type: published
              Y: 2024
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