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by G. David Garson (Author)
This is a graduate-level introduction and illustrated tutorial on partial least squares (PLS). PLS may be used in the context of variance-based structural equation modeling, in contrast to the usual covariance-based structural equation modeling, or in the context of implementing regression models. PLS is largely a nonparametric approach to modeling, not assuming normal distributions in the data, often recommended when the focus of research is prediction rather than hypothesis testing, when sample size is not large, or in the presence of noisy data.
Why we think the new edition is important:
* Covers the much-enhanced Version 3 of SmartPLS
* Now book length at 262 pages (was 139)
* Covers the traditional PLS algorithm and consistent PLS
* Covers bootstrapped PLS, consistent bootstrapped PLS, and PLS with blindfolding
* Covers confirmatory tetrad analysis
* Covers importance-performance map analysis (IPMA)
* Covers finite-mixture segmentation (FIMIX) and prediction-oriented segmentation (POS)
* Covers multi-group analysis (MGA)
* Covers significance testing with the permutation algorithm (MICOM)
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