After calculating the latent variables, other software uses OLS or bootstrap OLS method to measure the relationship between latent variables. Using OLS or bootstrap OLS method becomes a serious problem when the number of latent variables is large.

But where is the problem? The problem arises when the independent latent variables have a significant influence on each other. This issue is known in the statistical literature as multicollinearity.

For example, suppose you want to examine the effect of training and performance on the point of the game for a football team. In this example, Performance and Training are independent variables, and the Point is dependent. But it may distort the strong relationship between the two variables Performance and Training of the model results.  To solve this problem, Ridge Regression is suggested.

You can measure multicollinearity in the SEM-PLS software(Path Analysis, D8.2.8). Also, you can use the Ridge method to solve the multicollinearity problem(See Ridge  Regression and Path Analysis).