By Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt
Read Online or Download A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) PDF
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Extra info for A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
1 illustrates the three types of constructs and the relationships. The reputation construct on the far left is an independent (exogenous) variable. It is modeled as predicting the satisfaction construct. The satisfaction construct is an endogenous variable that has a dual relationship as both independent and depen dent. It is a dependent construct because it is predicted by reputation. But it is also an independent construct because it predicts loyalty. The loyalty construct on the far right is a dependent (endogenous) latent variable predicted by satisfaction.
Specifically, the process starts with the specification of structural and measurement models, followed by the collection and examination of data (Chapter 2). 10 An Introduction to Structural Equation Modeling 25 A Systematic Procedure for Applying PLS-SEM analyses (Chapter 3). On the basis of the results of the computation, the researcher then has to evaluate the results. To do so, researchers must know how to assess both reflective and formative measurement models (Chapters 4 and 5). When the data for the measures are con sidered reliable and valid (based on established criteria), researchers can then evaluate the structural model (Chapter 6).
Exhibit 2. 7 illustrates the key difference between the reflective and formative measurement perspective. The black circle illustrates the construct domain, which is the domain of content the construct is intended to measure. The gray circles represent the scope each indicator captures. Whereas the reflective measurement approach aims at maximizing the overlap between interchangeable indicators, the formative measurement approach tries to fully cover the con struct domain (black circle) by the different formative indicators (gray circles), which should have small overlap.
A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) by Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt