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May 15th 2012 | Kindle Edition | PDF, EPUB, FB2, DjVu, audiobook, mp3, RTF | 97 pages | ISBN: | 8.29 Mb

Partial least squares (PLS) analysis is an alternative to OLS regression, canonical correlation, or structural equation modeling (SEM) of systems of independent and response variables. In fact, PLS is sometimes called component-based SEM, inMorePartial least squares (PLS) analysis is an alternative to OLS regression, canonical correlation, or structural equation modeling (SEM) of systems of independent and response variables.

In fact, PLS is sometimes called component-based SEM, in contrast to covariance-based SEM, which is the usual type and which is implemented by Amos, LISREL, EQS and other major software packages. On the response side, PLS can relate the set of independent variables to multiple dependent (response) variables. On the predictor side, PLS can handle many independent variables, even when predictors display multicollinearity. PLS may be implemented as a regression model, predicting one or more dependents from a set of one or more independents- or it can be implemented as a path model, handling causal paths relating predictors as well as paths relating the predictors to the response variable(s).

PLS is implemented as a regression model by SPSS and by SASs PROC PLS. SmartPLS is the most prevalent implementation as a path model.PLS is characterized as a technique most suitable where the research purpose is prediction or exploratory modeling. In general, covariance-based SEM is preferred when the research purpose is confirmatory modeling.

PLS is less than satisfactory as an explanatory technique because it is low in power to filter out variables of minor causal importance (Tobias, 1997: 1).The advantages of PLS include ability to model multiple dependents as well as multiple independents- ability to handle multicollinearity among the independents- robustness in the face of data noise and missing data- and creating independent latents directly on the basis of crossproducts involving the response variable(s), making for stronger predictions.

Disadvantages of PLS include greater difficulty of interpreting the loadings of the independent latent variables (which are based on crossproduct relations with the response variables, not based as in common factor analysis on covariances among the manifest independents) and because the distributional properties of estimates are not known, the researcher cannot assess significance except through bootstrap induction. Overall, the mix of advantages and disadvantages means PLS is favored as a predictive technique and not as an interpretive technique, except for exploratory analysis as a prelude to an intepretive technique such as multiple linear regression or covariance-based structural equation modeling.

Hinseler, Ringle, and Sinkovics (2009: 282) thus state, PLS path modeling is recommended in an early stage of theoretical development in order to test and validate exploratory models.Table of ContentsOverview4Key Concepts and Terms5Background5Models6Regression vs. path models6PLS-DA models7Mixed methods7Reflective vs. formative models7Confirmatory vs. exploratory models7Inner (structural) model vs. outer (measurement) model8Variables8Measured factors and covariates8Modeled factors and response variables8Measurement level of variables10Parameter estimates11Cross-validation and goodness-of-fit11PRESS and optimal number of dimensions12PLS path modeling with SmartPLS13Creating a PLS project and importing data13Validating the data16Creating the path model in SmartPLS17Reflective vs.

formative models19Hiding the measurement model19Estimation options in SmartPLS19Finite mixture PLS20Running the path model in SmartPLS20Data metric for centered data21Weighting scheme22SmartPLS Output22Path coefficients22Bootstrapped significance23Options26Saving the model27SmartPLS Output27Model fit coeffi

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