Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
semPLS: Structural Equation Modeling Using Partial Least Squares | Monecke | Journal of Statistical Software
Authors: Armin Monecke, Friedrich Leisch
Title: semPLS: Structural Equation Modeling Using Partial Least Squares
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.

Page views:: 23425. Submitted: 2011-09-21. Published: 2012-05-24.
Paper: semPLS: Structural Equation Modeling Using Partial Least Squares     Download PDF (Downloads: 29034)
semPLS_1.0-8.tar.gz: R source package Download (Downloads: 792; 64KB)
v48i03.R: R example code from the paper Download (Downloads: 840; 2KB)

DOI: 10.18637/jss.v048.i03

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.