Download WarpPLS 5.0 User Manual - Collaborative for International

WarpPLS 5.0 User Manual
A. Introduction
Structural equation modeling (SEM) employing the partial least squares (PLS) method, or
PLS-based SEM for short, has been and continue being extensively used in a wide variety of
fields. Examples of fields in which PLS-based SEM is used are information systems (Guo et al.,
2011; Kock & Lynn, 2012), marketing (Biong & Ulvnes, 2011), international business (Ketkar et
al., 2012), nursing (Kim et al., 2012), medicine (Berglund et al., 2012), and global environmental
change (Brewer et al., 2012).
This software provides users with a wide range of features, several of which are not available
from other SEM software. For example, this software is the first and only (at the time of this
writing) to explicitly identify nonlinear functions connecting pairs of latent variables in SEM
models and calculate multivariate coefficients of association accordingly.
Additionally, this software is the first and only (at the time of this writing) to provide classic
PLS algorithms together with factor-based PLS algorithms for SEM (Kock, 2014). Factor-based
PLS algorithms generate estimates of both true composites and factors, fully accounting for
measurement error. They are equivalent to covariance-based SEM algorithms; but bring together
the “best of both worlds”, so to speak.
Factor-based PLS algorithms combine the precision of covariance-based SEM algorithms
under common factor model assumptions (Kock, 2014) with the nonparametric characteristics of
classic PLS algorithms. Moreover, factor-based PLS algorithms address head-on a problem that
has been discussed since the 1920s – the factor indeterminacy problem. Classic PLS algorithms
yield composites, as linear combinations of indicators, which can be seen as factor
approximations. Factor-based PLS algorithms, on the other hand, provide estimates of the true
factors, as linear combinations of indicators and measurement errors.
All of the features provided have been extensively tested with both “real” data, collected in
actual empirical studies, as well as simulated data generated through Monte Carlo procedures
(Robert & Casella, 2010). Future tests, however, may reveal new properties of these features,
and clarify the nature of existing properties.
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