Welcome to the WarpPLS blog!

Welcome to the WarpPLS blog! WarpPLS is a powerful structural equation modeling (SEM) software. It is commercialized by ScriptWarp Systems: www.scriptwarp.com.

Among other things, WarpPLS identifies nonlinear (or “warped”, hence the name of the software) relationships among latent variables and corrects the values of path coefficients accordingly. WarpPLS is arguably the first SEM software to do this.

Since most relationships between numeric variables are nonlinear, one could argue that WarpPLS finds the "real" relationships between latent variables in an SEM analysis. Typically path coefficients are increased, in some cases going from non-significant to significant at the P lower than 1 percent level.

The underlying algorithm employed by WarpPLS as its outer model default is partial least squares (PLS) regression, whose main characteristic is its ability to minimize multicollinearity among latent variables (even in the presence of overlapping manifest variables, or indicators). Other PLS-based outer model algorithms are also available, including PLS modes A and B.

Additionally, WarpPLS offers the following features, which are largely absent from most, if not all, PLS-based SEM software packages available today:

• It estimates P values for path coefficients automatically, instead of providing only standard errors or T values, and leaving the user to figure out what the corresponding P values are.
• It estimates several model fit indices, which have been designed to be meaningful in the context of PLS-based SEM analyses.
• It automatically builds the indicators’ product structure underlying moderating relationships, and goes a little further. It shows those moderating relationships, related path coefficients, and related P values in a model graph as they should be shown – that is, as links between latent variables and direct links. The latter connect pairs of latent variables, while the former connect latent variables and direct links between pairs of latent variables.
• It allows users to view scatter plots of each of the relationships among latent variables (when they are connected through arrows in the model), together with the curves that best approximate those relationships, and save those plots as .jpg files for inclusion in research reports.
• It provides a variety of graphs from which users can choose, including zoomed 2D graphs and 3D graphs; the latter for moderating effects. Both multivariate and bivariate relationship graphs are provided, for linear and nonlinear relationships, using standardized and unstandardized scales.
• It allows users to segment curves based on increments in the first derivative of the predictor latent variables on each of their criteria latent variables. This provides an alternative to data segmentation approaches such as FIMIX-PLS, without any reduction in sample.
• It calculates variance inflation factor (VIF) coefficients for latent variable predictors associated with each latent variable criterion. This allows users to check whether some predictors should be removed due to multicolinearity (this feature is particularly useful with latent variables that are measured based on only 1 or a few indicators).
• It calculates effect size coefficients analogous to Cohen’s f-squared coefficients for all paths. These are calculated as the absolute values of the individual contributions of the corresponding predictor latent variables to the R-square coefficients of the criterion latent variable in each latent variable block.
• It calculates indirect effects for paths with 2, 3 etc. segments; as well as total effects. The corresponding P values, standard errors, and effect sizes are also calculated. Indirect and total effects can be critical in the evaluation of downstream effects of latent variables that are mediated by other latent variables, especially in complex models with multiple mediating effects along concurrent paths.
• It calculates a variety of causality assessment coefficients, all of which are reported. These can be used in the assessment of the plausibility and direction of hypothesized cause-effect relationships.

These are only a few of the new features offered by WarpPLS.

Ned Kock

WarpPLS developer