A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
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  • For social science researchers with limited statistical and mathematical training, Hair (Kennesaw State U.), Hult, Ringle, and Sarstedt describe the fundamental aspects of a technique for structural equation modeling (SEM), the variance-based partial least squares SEM (PLS-SEM) approach, as an alternative to covariance-based (CB) SEM. They limit equations, formulas, Greek symbols, and other details, and instead focus on the basic fundamentals of PLS-SEM and outline general guidelines for understanding and evaluating the results of applying the method. Using a single case study on corporate reputation throughout, they explain structural equation modeling, the specification of structural and measurement models, the collection and examination of data, the PLS-SEM algorithm, considerations when running analyses, how to assess reflective and formative measurement models, how to evaluate the structural model, and advanced topics, such as mediating and moderating variables, hierarchical component models, testing for unobserved heterogeneity in sample data, invariance, and multigroup modeling. They include specific examples of estimating simple and complex PLS path models using SmartPLS software, which can be downloaded for free and used with the exercises in the book. Annotation c2013 Book News, Inc., Portland, OR (booknews.com)
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