semPLS: Structural Equation Modeling Using Partial Least ...

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SemPLS: Structural Equation Modeling UsingPartial Least SquaresArmin MoneckeLudwig Maximilians Universit atM unchenFriedrich LeischUniversit at f ur BodenkulturWienAbstractThis introduction to theRpackagesemPLSis a (slightly) modified version ofMoneckeand Leisch(2012), published in theJournal of Statistical Equation models (SEM) are very popular in many disciplines. The par-tial Least squares (PLS) approach to SEM offers an alternative to covariancebased SEM,which is especially suited for situations when data is not normally distributed. PLS pathmodelling is referred to assoft Modeling techniquewith minimum demands regardingmeasurement scales, sample sizes and residual distributions. ThesemPLSpackage pro-vides the capability to estimate PLS path models within theRprogramming setups for the estimation of factor scores can be used.

This introduction to the Rpackage semPLSis a (slightly) modified version of Monecke and Leisch (2012), published in the Journal of Statistical Software. Structural equation models (SEM) are very popular in many disciplines.

  Introduction, Using, Modeling, Structural, Equations, Partial, Mlsep, Structural equation, Structural equation modeling using partial