Consistent and asymptotically normal PLS estimators for linear structural equations
A vital extension to partial least squares (PLS) path modeling is introduced: consistency. While maintaining all the strengths of PLS, the consistent version provides two key improvements. Path coefficients, parameters of simultaneous equations, construct correlations, and indicator loadings are est...
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| Published in | Computational statistics & data analysis Vol. 81; pp. 10 - 23 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.01.2015
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| Subjects | |
| Online Access | Get full text |
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| Abstract | A vital extension to partial least squares (PLS) path modeling is introduced: consistency. While maintaining all the strengths of PLS, the consistent version provides two key improvements. Path coefficients, parameters of simultaneous equations, construct correlations, and indicator loadings are estimated consistently. The global goodness-of-fit of the structural model can also now be assessed, which makes PLS suitable for confirmatory research. A Monte Carlo simulation illustrates the new approach and compares it with covariance-based structural equation modeling.
•Consistent PLS estimates path coefficients and indicator loadings consistently.•Consistent PLS can estimate parameters of nonrecursive structural equation models.•A family of goodness-of-fit measures makes PLS suitable for confirmatory research.•Consistent PLS performs comparably to covariance-based structural equation modeling. |
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| AbstractList | A vital extension to partial least squares (PLS) path modeling is introduced: consistency. While maintaining all the strengths of PLS, the consistent version provides two key improvements. Path coefficients, parameters of simultaneous equations, construct correlations, and indicator loadings are estimated consistently. The global goodness-of-fit of the structural model can also now be assessed, which makes PLS suitable for confirmatory research. A Monte Carlo simulation illustrates the new approach and compares it with covariance-based structural equation modeling.
•Consistent PLS estimates path coefficients and indicator loadings consistently.•Consistent PLS can estimate parameters of nonrecursive structural equation models.•A family of goodness-of-fit measures makes PLS suitable for confirmatory research.•Consistent PLS performs comparably to covariance-based structural equation modeling. A vital extension to partial least squares (PLS) path modeling is introduced: consistency. While maintaining all the strengths of PLS, the consistent version provides two key improvements. Path coefficients, parameters of simultaneous equations, construct correlations, and indicator loadings are estimated consistently. The global goodness-of-fit of the structural model can also now be assessed, which makes PLS suitable for confirmatory research. A Monte Carlo simulation illustrates the new approach and compares it with covariance-based structural equation modeling. |
| Author | Henseler, Jörg Dijkstra, Theo K. |
| Author_xml | – sequence: 1 givenname: Theo K. surname: Dijkstra fullname: Dijkstra, Theo K. email: t.k.dijkstra@rug.nl organization: University of Groningen, NL, Department of Economics and Econometrics, The Netherlands – sequence: 2 givenname: Jörg orcidid: 0000-0002-9736-3048 surname: Henseler fullname: Henseler, Jörg email: joerg@henseler.com organization: University of Twente, NL, Department of Design, Production and Management, The Netherlands |
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| Snippet | A vital extension to partial least squares (PLS) path modeling is introduced: consistency. While maintaining all the strengths of PLS, the consistent version... |
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| SubjectTerms | Consistency equations Goodness-of-fit least squares Monte Carlo method Partial least squares Recursiveness Structural equation modeling |
| Title | Consistent and asymptotically normal PLS estimators for linear structural equations |
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