Consistent and asymptotically normal PLS estimators for linear structural equations

• Published in: Computational statistics & data analysis Vol. 81; pp. 10 - 23
• Main Authors: Dijkstra, Theo K., Henseler, Jörg
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2015
• Subjects: Consistency; equations; Goodness-of-fit; least squares; Monte Carlo method; Partial least squares; Recursiveness; Structural equation modeling; Structural equation modeling; Recursiveness; Partial least squares; Goodness-of-fit; Consistency
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2014.07.008

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.