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Article: Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies

TitleMultivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies
Authors
Keywordshigher-order IRT
multivariate IRT
multi-group IRT
Bayesian estimation
individual participant data, meta-analysis
Issue Date2019
PublisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/psychology
Citation
Frontiers in Psychology, 2019, v. 10, p. article no. 1328 How to Cite?
AbstractMany clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries.
Persistent Identifierhttp://hdl.handle.net/10722/274437
ISSN
2017 Impact Factor: 2.089
2015 SCImago Journal Rankings: 1.244
PubMed Central ID

 

DC FieldValueLanguage
dc.contributor.authorMun, EY-
dc.contributor.authorHuo, Y-
dc.contributor.authorWhite, HR-
dc.contributor.authorSuzuki, S-
dc.contributor.authorde la Torre, J-
dc.date.accessioned2019-08-18T15:01:41Z-
dc.date.available2019-08-18T15:01:41Z-
dc.date.issued2019-
dc.identifier.citationFrontiers in Psychology, 2019, v. 10, p. article no. 1328-
dc.identifier.issn1664-1078-
dc.identifier.urihttp://hdl.handle.net/10722/274437-
dc.description.abstractMany clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries.-
dc.languageeng-
dc.publisherFrontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/psychology-
dc.relation.ispartofFrontiers in Psychology-
dc.rightsThis Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjecthigher-order IRT-
dc.subjectmultivariate IRT-
dc.subjectmulti-group IRT-
dc.subjectBayesian estimation-
dc.subjectindividual participant data, meta-analysis-
dc.titleMultivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies-
dc.typeArticle-
dc.identifier.emailde la Torre, J: jdltorre@hku.hk-
dc.identifier.authorityde la Torre, J=rp02159-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fpsyg.2019.01328-
dc.identifier.pmid31244727-
dc.identifier.pmcidPMC6582193-
dc.identifier.scopuseid_2-s2.0-85068668440-
dc.identifier.hkuros302292-
dc.identifier.volume10-
dc.identifier.spagearticle no. 1328-
dc.identifier.epagearticle no. 1328-
dc.publisher.placeSwitzerland-

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