Multicollinearity occurs when two or more predictors in the model are correlated and provide redundant information about the response. Multicollinearity was measured by variance inflation factors (VIF) and tolerance. If VIF value exceeding 4.0, or by tol- erance less than 0.2 then there is a problem with multicollinearity (Hair et al., 2010).

VIF is the reciprocal of the tolerance value ; small VIF values indicates low correlation among variables under ideal conditions VIF

Hello Alejandro,

I sympathise with you.  It would be nice if criteria were black and white.

In essence, I'd agree with Pratiksha.  The advice I was able to obtain some time ago, after looking at a number of sources, was that the VIF preferably should be 1 for each predictor in, say, a regression analysis. However, < 6 is regarded as OK by some, and some say < 5.

You might be right in suspecting that it depends on the researcher's criteria.  Under that principle, the lower the better, but you shouldn't be too concerned as long as your VIF is < 10.

Robert

Hello mates

Your arguments are in line with what I thought

Thank you.

Hello Alejandro,

I'm glad you feel reassured.  I might have added that as long as you are "open" about reporting your results (and as long as they are less than 10!), you should be OK.

Every good wish with your research.

Robt.

Thank you again :)

All opinions expressed have some truth. But, a VIF value of 14 is generally considered problematic.

Thank you both

Multicollinearity occurs when two or more predictors in the model are correlated and provide redundant information about the response. Multicollinearity was measured by variance inflation factors (VIF) and tolerance. If VIF value exceeding 4.0, or by tol- erance less than 0.2 then there is a problem with multicollinearity (Hair et al., 2010).

After reading some papers about multicollinearity (i.e., Knock & Lynn, 2012), I have realized that in variance-sem context the recommended threshold is 3.3, while 5.0 seems to be the most used in literature.

Regards

http://www.scriptwarp.com/warppls/pubs/Kock_Lynn_2012.pdf

Hello again mates:

For all those interested in being up-to-date in PLS-SEM, I strongly recommend reading this paper by Jörg Henseler, Hubona & Ray, specially when dealing with the goodness of fit. However, it covers all the new issues related to PLS-SEM.

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1), 2-20.

Regards

Hello everyone,

According to Hair et al. (1999), the maximun acceptable level of VIF is 10. A VIF value over 10 is a clear signal of multicollinearity. You also should to analyze the tolerance values to have a clear idea of the problem. Moreover, if you have multicollinearity problems, you could resolve it transforming the variables with Box Cox method.

Hmm, check out this: Quality & Quantity (2007) 41:673–690, DOI 10.1007/s11135-006-9018-6

Article A Caution Regarding Rules of Thumb for Variance Inflation Factors

Dear Alexander.

I liked the abstract. I will read the article with interest. Thank you.

Can anyone tell me whether to accept a model when the VIF value is 3.21 and R Sq(adj) is =94%. But, when running a correlation between these two independent variables, i received a p-value which was less than 0.05 r=0.83. Does it mean that the selected variables are multicollinearitate? Is it correct to accept the model

Do those variables measure very similar concepts ?

yes , both variables express the Window to wall ratio in east and west facades of buildings

Waruni Jayasinghe

So that's what happens. There is no multicollinearity because VIF is less than the threshold values. However, given that both concepts are very close conceptually, correlation is high (this also usually happens with conceps such as "satisfaction" and "loyalty", where a casual relationship occurs). In your results section, report also discriminant validity to guarantee both concepts are different . Bests.

see (Hair et al., 2010) for multicollinearity, if VIF value exceeds 4.0, or tolerance less than 0.2 is observed, then there is a problem with multicollinearity.

I am very interrested by this topic ( collinearity or multicollinearity).

Can someone can found the most update on the use of VIF( less than 5 or less than 10, in terms of cuttof point), the coefficient R between two suspeted variables ( less than 0.8)

Dear Pierre

VIF values should be less than 5 to guarantee collinearity is not an issue in your model. However, some researchers recommend it to be < 3.3 when applying PLS-SEM.

Thanks Alejandro

Dear Alejandro

According to Lee and Hong (2016), the tolerance ideally should be less than 1 and the VIF less than 3.

Kind regards

Rodney

Dear Rodney:

I agree with you. Updated guidelines on PLS-SEM also indicate that VIF values below 3 are ideally (Hair et al., 2018).

Article When to use and how to report the results of PLS-SEM

Thank you for the information provided.

Regards

Any citation for less than 5, less than 10?

Please do share the access link. Thanks :)

To ensure that multicollinearity is not driving our results, we check for the variance inflation factor (VIF) in all empirical tests. The VIFs are all less than 10, indicating that multicollinearity is not a serious concern (Kennedy 2008; Neter 1996).

Dear @Adelaja

Check the references in the first post and this one I also cited above:

When to use and how to report the results of PLS-SEM

Accepting VIF less than 5 or 10 depends on the number of explanatory variables involved. I found that VIF above 5 indicates severe collinearity when only two explanatory variables are involved.

VIF is the reciprocal of the tolerance value ; small VIF values indicates low correlation among variables under ideal conditions VIF

In my opinion, despite the diverse thresholds given by scholars which may all be valid depending on factors such as the number of variables, I'd advise that you consider the least of all (i.e

I read somewhere in a research paper said that VIF

Yuu Han

I have never read VIF has to be less than 2. In any case, you can refer to VIF < 3, as recommended here: Article When to use and how to report the results of PLS-SEM

Alejandro Ros-Gálvez Thank you so much

سعيدا جدا بقراءة سؤالك

Here is an useful publication on collinearity assessment in PLS-SEM studies:

Article Common method bias in PLS-SEM: A full collinearity assessment approach

Here is a helpful book where you can find further information about multicollinearity

Kutner M. J., Nachtsheim C. J., Neter J., & Li W. (2004). Applied Linear Statistical Models (5th ed.). New York: McGraw-Hill.

The higher the value, the greater the correlation of the variable with other variables. Values of more than 4 or 5 are sometimes regarded as being moderate to high, with values of 10 or more being regarded as very high. These numbers are just rules of thumb; in some contexts a VIF of 2 could be a great problem

VIF less than 5 is considered in some studies.

Dear Scholars, Greatly appreciate your help if could provide a recent journal article(s) to cite on how to report multicollinearity using SPSS focusing on explaining about tolerance and VIF including the cut off value (Example comparing value of tolerance to VIF) . My analysis of tolerance value is between 0.26 and 0.478 compared to VIF value between 2.904 and 3.851

Poh, it does not matter the software you use (SPSS, Stata, SmartPLS or other). Just indicate VIF values in your study are below 5 and include any reference in this thread (for example, Hair). Bests

Alejandro, thank you

Dear Prof Dr. Alejandro,

Thank you for sharing the earlier journal article from Kock (2015) which was

Common method bias in PLS-SEM: A full collinearity assessment approach

In the journal, Kock (2015) mentioned that the cut of value of not more than 3.3 for collinearity assessment approach to say free from common method bias.

My question is may I use hair et al focusing on cut of value not more than 5 for collinearity assessment to say free of common method bias.

Alejandro Ros-Gálvez

Below 3.3 for CMB in the context of PLS-SEM.

Dear Prof Dr. Alejandro,

To clarify, can i delete items that create CMB although those loading its significant. I

I tried deleting those items in a variable (Construct) that causes CMB although the loadings are significant. After deleted those few items that causes CMB, my construct (Variable) is free from CMB. I followed that suggestions from https://www.youtube.com/watch?v=J7eeu4O80_M&t=183s . Greatly appreciate for your help if you could share a journal article that explained the process of deleting those items that causes CMB. Thank you

Poh Wai Choo, I would take a look at Knock (2015), which provides some guidelines on how to assess full collinearity. With respect to your question, you previously indicated VIF values below 3.3 or 5.0. Also read Knock and Lynn (2012). Specifically chapter 11.3.

Article Lateral Collinearity and Misleading Results in Variance-Base...

Thank you Prof Dr. Alejandro Ros-Gálvez . Now I have a very clear picture. You have just made my day

VIF value should be below 5 to prove if there is no multicollinearity among indicators in PLS-SEM.