Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables.
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| Title: | Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables. |
|---|---|
| Authors: | Muthén, Bengt1, Asparouhov, Tihomir1 |
| Source: | Structural Equation Modeling. Jan-Mar2015, Vol. 22 Issue 1, p12-23. 12p. |
| Subjects: | Latent structure analysis, Common method variance, Drawing, Structural equation modeling, Mediation, Scientific method |
| Abstract: | Causal inference in mediation analysis offers counterfactually based causal definitions of direct and indirect effects, drawing on research by Robins, Greenland, Pearl, VanderWeele, Vansteelandt, Imai, and others. This type of mediation effect estimation is little known and seldom used among analysts using structural equation modeling (SEM). The aim of this article is to describe the new analysis opportunities in a way that is accessible to SEM analysts and show examples of how to perform the analyses. An application is presented with an extension to a latent mediator measured with multiple indicators. [ABSTRACT FROM AUTHOR] |
| Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd 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: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 101830690 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Muthén%2C+Bengt%22">Muthén, Bengt</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Asparouhov%2C+Tihomir%22">Asparouhov, Tihomir</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Structural+Equation+Modeling%22">Structural Equation Modeling</searchLink>. Jan-Mar2015, Vol. 22 Issue 1, p12-23. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Latent+structure+analysis%22">Latent structure analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Common+method+variance%22">Common method variance</searchLink><br /><searchLink fieldCode="DE" term="%22Drawing%22">Drawing</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+equation+modeling%22">Structural equation modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Mediation%22">Mediation</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+method%22">Scientific method</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Causal inference in mediation analysis offers counterfactually based causal definitions of direct and indirect effects, drawing on research by Robins, Greenland, Pearl, VanderWeele, Vansteelandt, Imai, and others. This type of mediation effect estimation is little known and seldom used among analysts using structural equation modeling (SEM). The aim of this article is to describe the new analysis opportunities in a way that is accessible to SEM analysts and show examples of how to perform the analyses. An application is presented with an extension to a latent mediator measured with multiple indicators. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Structural Equation Modeling is the property of Taylor & Francis Ltd 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=101830690 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10705511.2014.935843 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 12 Subjects: – SubjectFull: Latent structure analysis Type: general – SubjectFull: Common method variance Type: general – SubjectFull: Drawing Type: general – SubjectFull: Structural equation modeling Type: general – SubjectFull: Mediation Type: general – SubjectFull: Scientific method Type: general Titles: – TitleFull: Causal Effects in Mediation Modeling: An Introduction With Applications to Latent Variables. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Muthén, Bengt – PersonEntity: Name: NameFull: Asparouhov, Tihomir IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan-Mar2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 10705511 Numbering: – Type: volume Value: 22 – Type: issue Value: 1 Titles: – TitleFull: Structural Equation Modeling Type: main |
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