Robust partial least squares path modeling

Detalhes bibliográficos
Autor(a) principal: Schamberger, Tamara
Data de Publicação: 2020
Outros Autores: Schuberth, Florian, Henseler, Jörg, Dijkstra, Theo K.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/107263
Resumo: Schamberger, T., Schuberth, F., Henseler, J., & Dijkstra, T. K. (2020). Robust partial least squares path modeling. Behaviormetrika, 47(1), 307-334. https://doi.org/10.1007/s41237-019-00088-2
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spelling Robust partial least squares path modelingCompositesOutliersRobust consistent partial least squaresRobust correlationRobust partial least squares path modelingAnalysisApplied MathematicsClinical PsychologyExperimental and Cognitive PsychologySchamberger, T., Schuberth, F., Henseler, J., & Dijkstra, T. K. (2020). Robust partial least squares path modeling. Behaviormetrika, 47(1), 307-334. https://doi.org/10.1007/s41237-019-00088-2Outliers can seriously distort the results of statistical analyses and thus threaten the validity of structural equation models. As a remedy, this article introduces a robust variant of Partial Least Squares Path Modeling (PLS) and consistent Partial Least Squares (PLSc) called robust PLS and robust PLSc, respectively, which are robust against distortion caused by outliers. Consequently, robust PLS/PLSc allows to estimate structural models containing constructs modeled as composites and common factors even if empirical data are contaminated by outliers. A Monte Carlo simulation with various population models, sample sizes, and extents of outliers shows that robust PLS/PLSc can deal with outlier shares of up to 50 % without distorting the estimates. The simulation also shows that robust PLS/PLSc should always be preferred over its traditional counterparts if the data contain outliers. To demonstrate the relevance for empirical research, robust PLSc is applied to two empirical examples drawn from the extant literature.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNSchamberger, TamaraSchuberth, FlorianHenseler, JörgDijkstra, Theo K.2020-11-16T23:59:26Z2020-01-012020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article28application/pdfhttp://hdl.handle.net/10362/107263eng0385-7417PURE: 26409918https://doi.org/10.1007/s41237-019-00088-2info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:51:58Zoai:run.unl.pt:10362/107263Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:54.456969Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Robust partial least squares path modeling
title Robust partial least squares path modeling
spellingShingle Robust partial least squares path modeling
Schamberger, Tamara
Composites
Outliers
Robust consistent partial least squares
Robust correlation
Robust partial least squares path modeling
Analysis
Applied Mathematics
Clinical Psychology
Experimental and Cognitive Psychology
title_short Robust partial least squares path modeling
title_full Robust partial least squares path modeling
title_fullStr Robust partial least squares path modeling
title_full_unstemmed Robust partial least squares path modeling
title_sort Robust partial least squares path modeling
author Schamberger, Tamara
author_facet Schamberger, Tamara
Schuberth, Florian
Henseler, Jörg
Dijkstra, Theo K.
author_role author
author2 Schuberth, Florian
Henseler, Jörg
Dijkstra, Theo K.
author2_role author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Schamberger, Tamara
Schuberth, Florian
Henseler, Jörg
Dijkstra, Theo K.
dc.subject.por.fl_str_mv Composites
Outliers
Robust consistent partial least squares
Robust correlation
Robust partial least squares path modeling
Analysis
Applied Mathematics
Clinical Psychology
Experimental and Cognitive Psychology
topic Composites
Outliers
Robust consistent partial least squares
Robust correlation
Robust partial least squares path modeling
Analysis
Applied Mathematics
Clinical Psychology
Experimental and Cognitive Psychology
description Schamberger, T., Schuberth, F., Henseler, J., & Dijkstra, T. K. (2020). Robust partial least squares path modeling. Behaviormetrika, 47(1), 307-334. https://doi.org/10.1007/s41237-019-00088-2
publishDate 2020
dc.date.none.fl_str_mv 2020-11-16T23:59:26Z
2020-01-01
2020-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/107263
url http://hdl.handle.net/10362/107263
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0385-7417
PURE: 26409918
https://doi.org/10.1007/s41237-019-00088-2
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 28
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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