Latent Variable Path Modeling with Partial Least Squares

Partial Least Squares (PLS) is an estimation method and an algorithm for latent variable path (LVP) models. PLS is a component technique and estimates the latent variables as weighted aggregates. The implications of this choice are considered and compared to covariance structure techniques like LISREL, COSAN and EQS. The properties of special cases of PLS (regression, factor scores, structural equations, principal components, canonical correlation, hierarchical components, correspondence analysis, three-mode path and component analysis) are examined step by step and contribute to the understanding of the general PLS technique. The proof of the convergence of the PLS algorithm is extended beyond two-block models. Some 10 computer programs and 100 applications of PLS are referenced. The book gives the statistical underpinning for the computer programs PLS 1. 8, which is in use in some 100 university computer centers, and for PLS/PC. It is intended to be the background reference for the users of PLS 1. 8, not as textbook or program manual.

1 Basic Principles of Model Building. - 2 The Basic and the Extended PLS Method. - 3 Foundations of Partial Least Squares. - 4 Mixed Measurement Level Multivariate Data. - 5 Predictive vs. Structural Modeling: PLS vs. ML. - 6 Latent Variables Three-Mode Path (LVP3) Analysis. - 7 PLS Programs and Applications. - Author Index.