Version 3.0 of WarpPLS is now available!

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!