Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model.
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| Title: | Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model. |
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| Authors: | Finch, W. Holmes1 |
| Source: | Structural Equation Modeling. Jan-Mar2015, Vol. 22 Issue 1, p60-75. 16p. |
| Subjects: | Common method variance, Variance inflation factors (Statistics), Latent structure analysis, Harmonic drives, Polynomials |
| Abstract: | Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed. [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: 101830687 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Finch%2C+W%2E+Holmes%22">Finch, W. Holmes</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, p60-75. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Common+method+variance%22">Common method variance</searchLink><br /><searchLink fieldCode="DE" term="%22Variance+inflation+factors+%28Statistics%29%22">Variance inflation factors (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Latent+structure+analysis%22">Latent structure analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Harmonic+drives%22">Harmonic drives</searchLink><br /><searchLink fieldCode="DE" term="%22Polynomials%22">Polynomials</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10705511.2014.935749 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 60 Subjects: – SubjectFull: Common method variance Type: general – SubjectFull: Variance inflation factors (Statistics) Type: general – SubjectFull: Latent structure analysis Type: general – SubjectFull: Harmonic drives Type: general – SubjectFull: Polynomials Type: general Titles: – TitleFull: Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Finch, W. Holmes 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 |
| ResultId | 1 |
