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Article: A Partially Confirmatory Approach to Scale Development With the Bayesian Lasso
Title | A Partially Confirmatory Approach to Scale Development With the Bayesian Lasso |
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Authors | |
Keywords | Partially confirmatory Lasso loading Residual covariance Factor analysis Bayesian Lasso |
Issue Date | 2021 |
Publisher | American Psychological Association. The Journal's web site is located at http://www.apa.org/journals/met.html |
Citation | Psychological Methods, 2021, v. 26 n. 2, p. 210-235 How to Cite? |
Abstract | The exploratory and confirmatory approaches of factor analysis lie on two ends of a continuum of substantive input for scale development. Recent advancements in Bayesian regularization methods enable more flexibility in covering a wide range of the substantive continuum. Based on the Bayesian Lasso (least absolute shrinkage and selection operator) methods for the regression model and covariance matrix, this research proposes a partially confirmatory approach to address the loading and residual structures at the same time. With at least one specified loading per item, a one-step procedure can be applied to figure out both structures simultaneously. With a few specified loadings per factor, a two-step procedure is preferred to capture the model configuration correctly. In both cases, the Bayesian hierarchical formulation is implemented using Markov Chain Monte Carlo estimation with different Lasso or regular priors. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed approach across different situations. |
Persistent Identifier | http://hdl.handle.net/10722/288826 |
ISSN | 2023 Impact Factor: 7.6 2023 SCImago Journal Rankings: 4.235 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, Jinsong | - |
dc.contributor.author | Guo, Zhihan | - |
dc.contributor.author | Zhang, Lijin | - |
dc.contributor.author | Pan, Junhao | - |
dc.date.accessioned | 2020-10-12T08:05:58Z | - |
dc.date.available | 2020-10-12T08:05:58Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Psychological Methods, 2021, v. 26 n. 2, p. 210-235 | - |
dc.identifier.issn | 1082-989X | - |
dc.identifier.uri | http://hdl.handle.net/10722/288826 | - |
dc.description.abstract | The exploratory and confirmatory approaches of factor analysis lie on two ends of a continuum of substantive input for scale development. Recent advancements in Bayesian regularization methods enable more flexibility in covering a wide range of the substantive continuum. Based on the Bayesian Lasso (least absolute shrinkage and selection operator) methods for the regression model and covariance matrix, this research proposes a partially confirmatory approach to address the loading and residual structures at the same time. With at least one specified loading per item, a one-step procedure can be applied to figure out both structures simultaneously. With a few specified loadings per factor, a two-step procedure is preferred to capture the model configuration correctly. In both cases, the Bayesian hierarchical formulation is implemented using Markov Chain Monte Carlo estimation with different Lasso or regular priors. Both simulated and real data sets were analyzed to evaluate the validity, robustness, and practical usefulness of the proposed approach across different situations. | - |
dc.language | eng | - |
dc.publisher | American Psychological Association. The Journal's web site is located at http://www.apa.org/journals/met.html | - |
dc.relation.ispartof | Psychological Methods | - |
dc.subject | Partially confirmatory | - |
dc.subject | Lasso loading | - |
dc.subject | Residual covariance | - |
dc.subject | Factor analysis | - |
dc.subject | Bayesian Lasso | - |
dc.title | A Partially Confirmatory Approach to Scale Development With the Bayesian Lasso | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1037/met0000293 | - |
dc.identifier.scopus | eid_2-s2.0-85088457486 | - |
dc.identifier.hkuros | 316818 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 210 | - |
dc.identifier.epage | 235 | - |
dc.identifier.isi | WOS:000655413000005 | - |
dc.identifier.issnl | 1082-989X | - |