Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/28071
Título: Consistent and asymptotically normal PLS estimators for linear structural equations
Autor: Henseler, Joerg
Dijkstra, Theo K.
Palavras-chave: models
modeling
test
latent-variables
Goodness-of-fit
Structural
equation
Consistency
least
squares
least-squares
research
distributions
Partial
Recursiveness
management
systems
guidelines
sem
partial
statistics
Data: 1-Jan-2015
Resumo: 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. (C) 2014 The Authors Published by Elsevier B.V.
Descrição: ISI Document Delivery No.: AR1LU Times Cited: 0 Cited Reference Count: 58 Dijkstra, Theo K. Henseler, Jorg Elsevier science bv Amsterdam
Peer review: yes
URI: http://hdl.handle.net/10362/28071
DOI: http://dx.doi.org/10.1016/j.csda.2014.07.008
ISSN: 0167-9473
Aparece nas colecções:NIMS: MagIC - Artigos em revista internacional com arbitragem científica

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