Question
Asked 28th Jan, 2018

Multicollinearity test in PLS-SEM?

My study model has two Independent variables, one dependent and one mediating .
How to assess collinearity in PLS-SEM?

Most recent answer

20th May, 2021
Ritika Dongrey
in a reflective model, can the multicollinearity issues can be established by only seeing inner model VIF values?? My inner Vif values are showing no multicollinearity issue, while outer model has few multicollinearity issues. Please guide.
2 Recommendations

Popular answers (1)

29th Jan, 2018
Alejandro Ros-Gálvez
Universidad Internacional de La Rioja
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:
4 Recommendations

All Answers (10)

29th Jan, 2018
Alejandro Ros-Gálvez
Universidad Internacional de La Rioja
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:
4 Recommendations
30th Jan, 2018
Adonai José Lacruz
Instituto Federal do Espírito Santo
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).
2 Recommendations
30th Jan, 2018
Alejandro Ros-Gálvez
Universidad Internacional de La Rioja
As Adonai said, this regression to obtain the VIF values can be performed with statistical softare, such as SPSS or Stata.
Regards
1 Recommendation
30th Jan, 2018
Ali Nasser Al-Tahitah
USIM | Universiti Sains Islam Malaysia
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?
2 Recommendations
30th Jan, 2018
Adonai José Lacruz
Instituto Federal do Espírito Santo
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
2 Recommendations
30th Jan, 2018
Alejandro Ros-Gálvez
Universidad Internacional de La Rioja
VIF values you reported are under the threshold of 3, 5 or 10. Then, it seems there are no collinearity issues.
1 Recommendation
31st Jan, 2018
Ali Nasser Al-Tahitah
USIM | Universiti Sains Islam Malaysia
Noted with thanks.
2 Recommendations
23rd Jan, 2019
Nancy Odhiambo
African Institute for Mathematical Sciences South Africa
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
1 Recommendation
23rd Sep, 2020
S. Lesmana
Universitas Muhammadiyah Sumatera Utara
PLS SEM is a non-parametric data analysis method with a small sample size such that normality and classical assumptions are not an absolute requirement such that they can be avoided. If the sample is large, it is reasonable to use SEM with strict attention to normality and classical assumptions.
1 Recommendation
20th May, 2021
Ritika Dongrey
in a reflective model, can the multicollinearity issues can be established by only seeing inner model VIF values?? My inner Vif values are showing no multicollinearity issue, while outer model has few multicollinearity issues. Please guide.
2 Recommendations

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