Structural equation models: From paths to networks (Westland 2019)

Simple item page
dc.contributor.authorSarstedt, Markoen_NZ
dc.contributor.authorRingle, Christian M.
dc.date.accessioned2021-01-21T02:20:37Z
dc.date.available2021-01-21T02:20:37Z
dc.date.issued2020en_NZ
dc.description.abstractStructural equation modeling (SEM) is a statistical analytic framework that allows researchers to specify and test models with observed and latent (or unobservable) variables and their generally linear relationships. In the past decades, SEM has become a standard statistical analysis technique in behavioral, educational, psychological, and social science researchers’ repertoire. From a technical perspective, SEM was developed as a mixture of two statistical fields—path analysis and data reduction. Path analysis is used to specify and examine directional relationships between observed variables, whereas data reduction is applied to uncover (unobserved) low-dimensional representations of observed variables, which are referred to as latent variables. Since two different data reduction techniques (i.e., factor analysis and principal component analysis) were available to the statistical community, SEM also evolved into two domains—factor-based and component-based (e.g., Jöreskog and Wold 1982). In factor-based SEM, in which the psychometric or psychological measurement tradition has strongly influenced, a (common) factor represents a latent variable under the assumption that each latent variable exists as an entity independent of observed variables, but also serves as the sole source of the associations between the observed variables. Conversely, in component-based SEM, which is more in line with traditional multivariate statistics, a weighted composite or a component of observed variables represents a latent variable under the assumption that the latter is an aggregation (or a direct consequence) of observed variables.
dc.format.mimetypeapplication/pdf
dc.identifier.citationSarstedt, M., & Ringle, C. M. (2020). Structural equation models: From paths to networks (Westland 2019). Psychometrika, 85(3), 841–844. https://doi.org/10.1007/s11336-020-09719-0en
dc.identifier.doi10.1007/s11336-020-09719-0en_NZ
dc.identifier.eissn1860-0980en_NZ
dc.identifier.issn0033-3123en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14079
dc.language.isoenen_NZ
dc.publisherSpringeren_NZ
dc.relation.isPartOfPsychometrikaen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s11336-020-09719-0
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.subjectScience & Technologyen_NZ
dc.subjectSocial Sciencesen_NZ
dc.subjectPhysical Sciencesen_NZ
dc.subjectMathematics, Interdisciplinary Applicationsen_NZ
dc.subjectSocial Sciences, Mathematical Methodsen_NZ
dc.subjectPsychology, Mathematicalen_NZ
dc.subjectMathematicsen_NZ
dc.subjectMathematical Methods In Social Sciencesen_NZ
dc.subjectPsychologyen_NZ
dc.titleStructural equation models: From paths to networks (Westland 2019)en_NZ
dc.typeJournal Article
pubs.begin-page841
pubs.elements-id257028
pubs.end-page844
pubs.issue3en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/DMGT
pubs.organisational-group/Waikato/DMGT/DMGO
pubs.organisational-group/Waikato/DMGT/DMGO/DMGO Academics
pubs.publication-statusPublisheden_NZ
pubs.user.infoRingle, Christian (cringle@waikato.ac.nz)
pubs.volume85en_NZ
uow.verification.statusunverified
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Sarstedt-Ringle2020_Article_StructuralEquationModelsFromPa.pdf
Size:
210.83 KB
Format:
Adobe Portable Document Format
Description:
Published version
Thumbnail Image
Name: Sarstedt-Ringle2020_Article_StructuralEquationModelsFromPa.pdf
Size: 210.83 KB
Kind: Adobe PDF
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Research Commons Deposit Agreement 2017.pdf
Size:
188.11 KB
Format:
Adobe Portable Document Format
Description:
Name: Research Commons Deposit Agreement 2017.pdf
Size: 188.11 KB
Kind: Adobe PDF
Collections
Management Papers