Links to specific topics

(See also under "Labels" at the bottom-left area of this blog)
[ Welcome post ] [ Installation issues ] [ ] [ Posts with YouTube links ] [ PLS-SEM email list ]

Tuesday, July 13, 2010

Using WarpPLS for multiple regression analyses

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 nonparametric algorithms, resampling or "stable" algorithms, 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 typically use the algorithm 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 explicitly modeled. 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 and variants, which yield S curves; and Warp2 and variants, which yield U curves. Again, see the software's User Manual for more details.


Anonymous said...

I have a question concerning the comparison of two countries. Before calculating the p-values for the path coefficients I would like to check whether the weights do not have a significant p-value (as indicated in you paper 'Advanced mediating effects tests, multi-group analyses, and
measurement model assessments in PLS-based SEM'). However, my standard errors slightly differ for the different paths. Which of the standard erros should I use to calculate the p-value for the weights? Thank you very much for your help.

Kind regards

Ned Kock said...

Different standard errors are used for different groups, whether you are using the pooled standard error or Satterthwaite method. See also the sheet linked below: