Why should someone use WarpPLS for multiple regression analysis?
There are two main advantages of using WarpPLS to conduct a multiple regression analysis. The advantages are over a traditional multiple regression analysis, where the independent and dependent variables are measured through single indicators. With WarpPLS, this would be implemented through the creation of "latent" variables that would each be associated with a single indicator; which means that they would not be true latent variables in the sense normally assumed in structural equation modeling.
The first advantage is that the calculation of P values with WarpPLS is based on a nonparametric algorithm, resampling, and thus does not require that the variables be normally distributed. A traditional multiple regression analysis, on the other hand, requires that the variables be normally distributed. In this sense, WarpPLS can be seen as conducting a robust, or nonparametric, multiple regression analysis. This first advantage assumes that all one is doing is a plain linear analysis with WarpPLS, for which one would use the algorithms PLS Regression or Robust Path Analysis. See the software's User Manual for more details.
The second advantage is that WarpPLS allows for nonlinear relationships between the independent and dependent variables to be analyzed. This provides a much richer view of the associations between variables, and sometimes leads to path coefficients that are different from (often higher than) those obtained through a linear analysis (as in a traditional multiple regression analysis). The nonlinear analysis algorithms available are Warp3 PLS Regression (which yields S curves) and Warp2 PLS Regression (which yields U curves). Again, see the software's User Manual for more details.