Author
Listed:
- Sergio Venturini
(Università Cattolica del Sacro Cuore)
- Mehmet Mehmetoglu
(Norwegian University of Science and Technology)
- Hengky Latan
(FTD Institute
HLC Consulting)
Abstract
As a result of its ability to deal with situations that are difficult to address using other SEM methods, the partial least squares (PLS) approach to structural equation modeling (SEM)Partial Least Squares (PLS)structural equation model (PLS-SEM) has attracted a lot of attention in recent years from applied researchers and practitioners in various fields. One reason for this growth in interest is represented by the many theoretical contributions emerging from the PLS-SEM research community, which have allowed us to deepen our knowledge of the method and extend its capabilities into new contexts. However, these contributions would have remained confined to academic journals if not for a parallel and similar development in the software packages available to implement these methodological innovations. Indeed, it is a well-known fact in the history of PLS-SEM that the lack of advanced and user-friendly software has been the main reason for the delay in the diffusion of this method in the applied sciences. Fortunately, we find ourselves nowadays in the opposite situation, as many high-quality packages for performing all varieties of PLS-SEM analyses have become available. In this chapter we present an updated review of the most popular commercial and open-source software packages for PLS-SEM. In particular, we discuss and compare ADANCOAdvanced Analysis of Composites (ADANCO), SmartPLSPartial Least Squares (PLS)SmartPLS, WarpPLSPartial Least Squares (PLS)WarpPLS, XLSTAT-PLSPM, theXLSTAT-PLSPM plssem package for Stata, and the cSEMCSEM and SEMinRSEMinR packages for R. Using a publicly available data set, we briefly illustrate the main features of each of these software packages and examine their corresponding strengths and weaknesses.
Suggested Citation
Sergio Venturini & Mehmet Mehmetoglu & Hengky Latan, 2023.
"Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review,"
Springer Books, in: Hengky Latan & Joseph F. Hair, Jr. & Richard Noonan (ed.), Partial Least Squares Path Modeling, edition 2, chapter 0, pages 113-152,
Springer.
Handle:
RePEc:spr:sprchp:978-3-031-37772-3_5
DOI: 10.1007/978-3-031-37772-3_5
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