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Authors: Armin Monecke, Friedrich Leisch
Title: [download]
(12398)
semPLS: Structural Equation Modeling Using Partial Least Squares
Reference: Vol. 48, Issue 3, May 2012
Submitted 2011-09-21, Accepted 2012-04-16
Type: Article
Abstract:

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.

Paper: [download]
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semPLS: Structural Equation Modeling Using Partial Least Squares
(application/pdf, 992.8 KB)
Supplements: [download]
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semPLS_1.0-8.tar.gz: R source package
(application/x-gzip, 64.5 KB)
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v48i03.R: R example code from the paper
(application/octet-stream, 3 KB)
Resources: BibTeX | OAI
Creative Commons License
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3)
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