Robustness of partial least-squares method for estimating latent variable quality structures
AbstractLatent variable structural models and the partial least-squares (PLS) estimation procedure have found increased interest since being used in the context of customer satisfaction measurement. The well-known property that the estimates of the inner structure model are inconsistent implies biased estimates for finite sample sizes. A simplified version of the structural model that is used for the Swedish Customer Satisfaction Index (SCSI) system has been used to generate simulated data and to study the PLS algorithm in the presence of three inadequacies: (i) skew instead of symmetric distributions for manifest variables; (ii) multi-collinearity within blocks of manifest and between latent variables; and (iii) misspecification of the structural model (omission of regressors). The simulation results show that the PLS method is quite robust against these inadequacies. The bias that is caused by the inconsistency of PLS estimates is substantially increased only for extremely skewed distributions and for the erroneous omission of a highly relevant latent regressor variable. The estimated scores of the latent variables are always in very good agreement with the true values and seem to be unaffected by the inadequacies under investigation.
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Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Journal of Applied Statistics.
Volume (Year): 26 (1999)
Issue (Month): 4 ()
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Web page: http://taylorandfrancis.metapress.com/link.asp?target=journal&id=100411
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- Dijkstra, Theo, 1983. "Some comments on maximum likelihood and partial least squares methods," Journal of Econometrics, Elsevier, vol. 22(1-2), pages 67-90.
- Ringle, Christian M. & Götz, Oliver & Wetzels, Martin & Wilson, Bradley, 2009.
"On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies,"
014, Maastricht : METEOR, Maastricht Research School of Economics of Technology and Organization.
- Ringle, Christian M. & Götz, Oliver & Wetzels, Martin & Wilson, Bradley, 2009. "On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies," MPRA Paper 15390, University Library of Munich, Germany.
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