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Article: An Empirical Evaluation of Mediation Effect Analysis With Manifest and Latent Variables Using Markov Chain Monte Carlo and Alternative Estimation Methods

TitleAn Empirical Evaluation of Mediation Effect Analysis With Manifest and Latent Variables Using Markov Chain Monte Carlo and Alternative Estimation Methods
Authors
KeywordsBayesian inference
mediation effect
latent variables
manifest variables
Markov chain Monte Carlo
Issue Date2014
Citation
Structural Equation Modeling, 2014, v. 21, n. 2, p. 253-262 How to Cite?
AbstractRecently, the Markov chain Monte Carlo (MCMC) estimation method has become explosively popular in a variety of quantitative research methods. In mediation effect analysis (MEA), the MCMC estimation methods can be a promising tool and an important alternative as compared with traditional methods (e.g., the z test using the delta method and the bias-corrected bootstrapping method) in addressing issues such as nonconvergence and complex modeling. In this article, a subject-level MCMC approach for the single MEA is empirically evaluated and compared with traditional methods through Monte Carlo simulation. The evaluation covers point and interval estimates of both manifest and latent variables across conditions including sample size, effect size, and magnitude of factor loadings. BUGS codes for MEA with both manifest and latent variables are provided that can be easily adapted to fit various MEA models in practice. © Taylor & Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/288624
ISSN
2023 Impact Factor: 2.5
2023 SCImago Journal Rankings: 3.647
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Jinsong-
dc.contributor.authorChoi, Jaehwa-
dc.contributor.authorWeiss, Brandi A.-
dc.contributor.authorStapleton, Laura-
dc.date.accessioned2020-10-12T08:05:26Z-
dc.date.available2020-10-12T08:05:26Z-
dc.date.issued2014-
dc.identifier.citationStructural Equation Modeling, 2014, v. 21, n. 2, p. 253-262-
dc.identifier.issn1070-5511-
dc.identifier.urihttp://hdl.handle.net/10722/288624-
dc.description.abstractRecently, the Markov chain Monte Carlo (MCMC) estimation method has become explosively popular in a variety of quantitative research methods. In mediation effect analysis (MEA), the MCMC estimation methods can be a promising tool and an important alternative as compared with traditional methods (e.g., the z test using the delta method and the bias-corrected bootstrapping method) in addressing issues such as nonconvergence and complex modeling. In this article, a subject-level MCMC approach for the single MEA is empirically evaluated and compared with traditional methods through Monte Carlo simulation. The evaluation covers point and interval estimates of both manifest and latent variables across conditions including sample size, effect size, and magnitude of factor loadings. BUGS codes for MEA with both manifest and latent variables are provided that can be easily adapted to fit various MEA models in practice. © Taylor & Francis Group, LLC.-
dc.languageeng-
dc.relation.ispartofStructural Equation Modeling-
dc.subjectBayesian inference-
dc.subjectmediation effect-
dc.subjectlatent variables-
dc.subjectmanifest variables-
dc.subjectMarkov chain Monte Carlo-
dc.titleAn Empirical Evaluation of Mediation Effect Analysis With Manifest and Latent Variables Using Markov Chain Monte Carlo and Alternative Estimation Methods-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/10705511.2014.882688-
dc.identifier.scopuseid_2-s2.0-84898060326-
dc.identifier.volume21-
dc.identifier.issue2-
dc.identifier.spage253-
dc.identifier.epage262-
dc.identifier.eissn1532-8007-
dc.identifier.isiWOS:000334069700007-
dc.identifier.issnl1070-5511-

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