[AISWorld] Version 3.0 of WarpPLS is now available!

[Ned Kock]

Dear colleagues:

 

Version 3.0 of WarpPLS is now available! You can download and install it for
a free 90-day trial from:

 

http://warppls.com

 

The full User Manual is also available for download from the web site above
separately from the software.

 

Some important notes for users of previous versions:

 

- Version 2.0 users can use the same license information that they already
have; it will work for version 3.0 for the remainder of their license
periods.

 

- Project files generated with previous versions are automatically converted
to version 3.0 project files. Users are notified of that by the software,
and given the opportunity not to convert the files if they so wish.

 

- The MATLAB Compiler Runtime 7.14, used in this version, is the same as the
one used in version 2.0. Therefore, if you already have WarpPLS 2.0
installed on your computer, you should uncheck the Runtime component on the
installer (i.e., the self-installing .exe file). The same Runtime cannot be
installed twice on the same computer.

 

WarpPLS is a powerful PLS-based structural equation modeling (SEM) software.
Since its first release in 2009, its user base has grown steadily, with more
than 5,000 users worldwide today.

 

Some of its most distinguishing features are the following:

 

- It is easy to use, with a step-by-step user interface guide.

 

- It identifies nonlinear relationships, and estimates path coefficients
accordingly. 

 

- It also models linear relationships, using a standard PLS regression
algorithm.

 

- It models reflective and formative variables, as well as moderating
effects.

 

- It calculates P values, model fit indices, and collinearity estimates.

 

Below is a list of new features in this version. The User Manual has more
details on how these new features can be useful in SEM analyses.

 

- Addition of latent variables as indicators. Users now have the option of
adding latent variable scores to the set of standardized indicators used in
an SEM analysis.

 

- Blindfolding. Users now have the option of using a third resampling
algorithm, namely blindfolding, in addition to bootstrapping and
jackknifing.

 

- Effect sizes. Cohen's f-squared effect size coefficients are now
calculated and shown for all path coefficients.

 

- Estimated collinearity. Collinearity is now estimated before the SEM
analysis is run. When collinearity appears to be too high, users are warned
about it.

 

- Full collinearity VIFs. VIFs are now shown for all latent variables,
separately from the VIFs calculated for predictor latent variables in
individual latent variable blocks.

 

- Indirect and total effects. Indirect and total effects are now calculated
and shown, together with the corresponding P values, standard errors, and
effect sizes.

 

- P values for all weights and loadings. P values are now shown for all
weights and loadings, including those associated with indicators that make
up moderating variables.

 

- Predictive validity. Stone-Geisser Q-squared coefficients are now
calculated and shown for each endogenous variable in an SEM model.

 

- Ranked data. Users can now select an option to conduct their analyses with
only ranked data, whereby all the data is automatically ranked prior to the
SEM analysis (the original data is retained in unranked format).

 

- Restricted ranges. Users can now run their analyses with subsamples
defined by a range restriction variable, which may be standardized or
unstandardized.

 

- Standard errors for all weights and loadings. Standard errors are now
shown for all loadings and weights.

 

- VIFs for all indicators. VIFs are now shown for all indicators, including
those associated with moderating latent variables.

 

Enjoy!

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