Partial least squares path modeling: Updated guidelines

Jörg Henseler, Geoffrey Hubona, Pauline Ash Ray

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

16 Citations (Scopus)

Abstract

Partial least squares (PLS) path modeling is a variance-based structural equation modeling technique that is widely applied in business and social sciences. It is the method of choice if a structural equation model contains both factors and composites. This chapter aggregates new insights and offers a fresh look at PLS path modeling. It presents the newest developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations (HTMT). PLS path modeling can be regarded as an instantiation of generalized canonical correlation analysis. It aims at modeling relationships between composites, i.e., linear combinations of observed variables. A recent extension, consistent PLS, makes it possible to also include factors in a PLS path model. The chapter illustrates how to specify a PLS path model consisting of construct measurement and structural relationships. It also shows how to integrate categorical variables. A particularly important consideration is model identification: Every construct measured by multiple indicators must be embedded into a nomological net, which means that there must be at least one other construct with which it is related. PLS path modeling results are useful for exploratory and confirmatory research. The chapter provides guidelines for assessing the fit of the overall model, the reliability and validity of the measurement model, and the relationships between constructs.
Original languageEnglish
Title of host publicationPartial Least Squares Path Modeling
Subtitle of host publicationBasic Concepts, Methodological Issues and Applications
EditorsHengky Latan, Richard Noonan
Place of PublicationCham
PublisherSpringer
Pages19-39
Number of pages21
ISBN (Electronic)978-3-319-64069-3
ISBN (Print)978-3-319-64068-6
DOIs
Publication statusPublished - 2017

Fingerprint

Partial least squares
Modeling
Path model
Factors
Canonical correlation analysis
Social sciences
Measurement model
Structural equation modeling
Construct measurement
Categorical variables
Structural equation model

Cite this

Henseler, J., Hubona, G., & Ray, P. A. (2017). Partial least squares path modeling: Updated guidelines. In H. Latan, & R. Noonan (Eds.), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (pp. 19-39). Cham: Springer. https://doi.org/10.1007/978-3-319-64069-3_2
Henseler, Jörg ; Hubona, Geoffrey ; Ray, Pauline Ash. / Partial least squares path modeling : Updated guidelines. Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. editor / Hengky Latan ; Richard Noonan. Cham : Springer, 2017. pp. 19-39
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Henseler, J, Hubona, G & Ray, PA 2017, Partial least squares path modeling: Updated guidelines. in H Latan & R Noonan (eds), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. Springer, Cham, pp. 19-39. https://doi.org/10.1007/978-3-319-64069-3_2

Partial least squares path modeling : Updated guidelines. / Henseler, Jörg ; Hubona, Geoffrey; Ray, Pauline Ash.

Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. ed. / Hengky Latan; Richard Noonan. Cham : Springer, 2017. p. 19-39.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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Henseler J, Hubona G, Ray PA. Partial least squares path modeling: Updated guidelines. In Latan H, Noonan R, editors, Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications. Cham: Springer. 2017. p. 19-39 https://doi.org/10.1007/978-3-319-64069-3_2