semPLS: Structural Equation Modeling Using Partial Least Squares

By A. Monecke, Friedrich Leisch. 2012

527 citations recorded by the source corpus.

Structural equation models (SEM) are very popular in many disciplines. The partial least squares (PLS) approach to SEM offers an alternative to covariance-based SEM, which is especially suited for situations when data is not normally distributed. PLS path modelling is referred to as soft-modeling-technique with minimum demands regarding mea- surement scales, sample sizes and residual distributions. The semPLS package provides the capability to estimate PLS path models within the R programming environment. Different setups for the estimation of factor scores can be used. Furthermore it contains modular methods for computation of bootstrap confidence intervals, model parameters and several quality indices. Various plot functions help to evaluate the model. The well known mobile phone dataset from marketing research is used to demonstrate the features of the package.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/867a4cb18dc781f3cd139510e6c3a56deaf4c4f7

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Citations in the corpus

Applications of structural equation modeling (SEM) in ecological studies: an updated review (2016, background): “continue collecting long-term data while updating their hypotheses (Monecke and Leisch 2012).”
Partial Least Squares Structural Equation Modeling with R. (2016, methodology): “Currently, there are several alternative PLS-SEM software programs such as SPAD-PLS , VisualPLS (Fu, 2006), PLS-GUI (Li, 2005), PLS-Graph (Chin, 1993– 2003), SmartPLS (Hansmann, & Ringle, 2004), as well as the R packages plspm (Sanches, Trinchera, & Russolillo, 2015) and semPLS (Monecke, 2013).”
Testing measurement invariance of composites using partial least squares (2016, methodology): “The statistical software R (R Core Team, 2014), the semPLS package (Monecke and Leisch, 2012), and our MICOM code in R allow us to conduct the computations of Step 2 and the subsequent Step 3.”
A new criterion for assessing discriminant validity in variance-based structural equation modeling (2015, methodology): “In essence, as suggested by Nunnally (1978) and Netemeyer et al. (2003), the HTMT approach is an estimate of the correlation between the constructs ξi and ξj (see the Appendix for the derivation), which parallels the disattenuated construct score correlation.”
Goodness-of-fit indices for partial least squares path modeling (2013, methodology): “…modeling software with graphical user-interfaces, like SmartPLS (Ringle et al. 2005), PLS-Graph (Soft Modeling Inc 1992–2002) or the PLS-PM module of XLSTAT software (Addinsoft SARL 2007–2008), and open packages like semPLS (Monecke and Leisch 2012) have contributed to PLS path modeling’s appeal.”
Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods (2017, methodology): “PLS estimations draw on the semPLS (Monecke and Leisch 2012) package, while the GSCA estimations rely on the ASGSCA (Romdhani et al.”
Estimation issues with PLS and CBSEM: Where the bias lies! ☆ (2016, methodology): “To rule out potential problems resulting from the PLSc implementation of the matrixpls package, we re-ran the simulation study using our PLSc extension of the semPLS package (Monecke & Leisch, 2012).”