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- Publisher Website: 10.1080/10705511.2020.1854763
- Scopus: eid_2-s2.0-85099827698
- WOS: WOS:000611645600001
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Article: A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step
Title | A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step |
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Authors | |
Keywords | Factor analysis bayesian regularization factor extraction bilevel selection spike and slab prior |
Issue Date | 2021 |
Publisher | Psychology Press. The Journal's web site is located at http://www.tandfonline.com/hsem |
Citation | Structural Equation Modeling, 2021, v. 28 n. 4, p. 518-528 How to Cite? |
Abstract | This research proposes a one-step Bayesian regularized approach to exploratory factor analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized exploratory factor analysis (BREFA) model builds on the idea of bi-level Bayesian sparse group selection and can produce exact zero estimates at both the factor and loading levels. It can distinguish true factors from spurious factors and provide estimations of model and tuning parameters simultaneously. In addition to achieving model simplicity at both the factor and item levels, the approach provides interval estimates that can be used for significance testing, making it capable of addressing both uncorrelated and correlated factors. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation with the multivariate spike and slab priors and posterior median estimator. Based on simulated and real data analysis, BREFA demonstrates clear advantages or flexibility compared with traditional and Bayesian EFA, in terms of factor extraction, parameter estimation, and model interpretation. |
Persistent Identifier | http://hdl.handle.net/10722/304768 |
ISSN | 2021 Impact Factor: 6.181 2020 SCImago Journal Rankings: 4.041 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, J | - |
dc.date.accessioned | 2021-10-05T02:34:54Z | - |
dc.date.available | 2021-10-05T02:34:54Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Structural Equation Modeling, 2021, v. 28 n. 4, p. 518-528 | - |
dc.identifier.issn | 1070-5511 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304768 | - |
dc.description.abstract | This research proposes a one-step Bayesian regularized approach to exploratory factor analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized exploratory factor analysis (BREFA) model builds on the idea of bi-level Bayesian sparse group selection and can produce exact zero estimates at both the factor and loading levels. It can distinguish true factors from spurious factors and provide estimations of model and tuning parameters simultaneously. In addition to achieving model simplicity at both the factor and item levels, the approach provides interval estimates that can be used for significance testing, making it capable of addressing both uncorrelated and correlated factors. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation with the multivariate spike and slab priors and posterior median estimator. Based on simulated and real data analysis, BREFA demonstrates clear advantages or flexibility compared with traditional and Bayesian EFA, in terms of factor extraction, parameter estimation, and model interpretation. | - |
dc.language | eng | - |
dc.publisher | Psychology Press. The Journal's web site is located at http://www.tandfonline.com/hsem | - |
dc.relation.ispartof | Structural Equation Modeling | - |
dc.rights | Accepted Manuscript (AM) i.e. Postprint This is an Accepted Manuscript of an article published by Taylor & Francis in [JOURNAL TITLE] on [date of publication], available online: http://www.tandfonline.com/[Article DOI]. | - |
dc.subject | Factor analysis | - |
dc.subject | bayesian regularization | - |
dc.subject | factor extraction | - |
dc.subject | bilevel selection | - |
dc.subject | spike and slab prior | - |
dc.title | A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step | - |
dc.type | Article | - |
dc.identifier.email | Chen, J: jinsong@hku.hk | - |
dc.identifier.authority | Chen, J=rp02740 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/10705511.2020.1854763 | - |
dc.identifier.scopus | eid_2-s2.0-85099827698 | - |
dc.identifier.hkuros | 325771 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 518 | - |
dc.identifier.epage | 528 | - |
dc.identifier.isi | WOS:000611645600001 | - |
dc.publisher.place | United States | - |