After running the pls algorithm, just take a look at the results. SmartPLS 3.0 gives you this information automatically. VIF values are under "Collinearity Statistics (VIF)". To interpret this information, read this thread:

https://www.researchgate.net/post/Multicollinearity_issues_is_a_value_less_than_10_acceptable_for_VIF?_ec=topicPostOverviewAuthoredQuestions

After running the pls algorithm, just take a look at the results. SmartPLS 3.0 gives you this information automatically. VIF values are under "Collinearity Statistics (VIF)". To interpret this information, read this thread:

https://www.researchgate.net/post/Multicollinearity_issues_is_a_value_less_than_10_acceptable_for_VIF?_ec=topicPostOverviewAuthoredQuestions

In addition @Alejandro Ros-Galvez , evaluate the collinearity in the inner and outer (if formative measure) models with the same evaluation measures: generally we consider tolerance values below 0.2 or VIF above 5 as levels critical of collinearity.

In SmartPLS gives VIF automatically. But in the package plssem for R you have to calculate. But it is an easy solver. You could run regression with a latent variable (score) and calculate the VIF (see VIF function).

As Adonai said, this regression to obtain the VIF values can be performed with statistical softare, such as SPSS or Stata.

Regards

Thank you so much, Dear Alejandro and Adonai for answer my Question,

I have been assessed the collinearity through SPSS,

The tolerance result of three variables was (.59, .79, and .70), while the VIF was ( 1.67, 1.27, 1.47).

So, Is that enough justification to avoid the Multi-collinearity problem?

I think so. But see too condition index (CI). Consider CI values above 30 critical levels of collinearity. The CI < 30 indicated that the variables would not present collinearity problems if they stayed together (Gujarati, 2003).

Note. You can use VIF or TOL. TOL is merely the inverse of VIF; that is TOL = (1/VIF).

Ref.

Gujarati, D. N. (2003). Basic econometrics. New York: McGraw-Hill

VIF values you reported are under the threshold of 3, 5 or 10. Then, it seems there are no collinearity issues.

Noted with thanks.

I am trying to check for multicollinearity in R but am not winning. I have 4 times for the depend variable and 10 items for the in-dependend variable. I am fitting PLS-SEM and using plspm package in R