forum.smartpls.com

Dear all,
I have searched the web already but could not find an answer to my question.

My Q2 included and Q2 excluded are both positive, but since the value of Q2included is smaller, the q2 is negative.
I wondered what does it mean and how should I interpret it.

Thank you for your help.

It could mean that your model is overfitting to the data and thus it has a better predictive releavance without the focal predictor.

However, there are quite many issues with the q² (small q). You need to consider that your context of the model changes and also that the data imputation in the blindfolding procedure might change.
This is a very under-researched field and thats why we do not provide an automatic calculation of the q² effect size.

I would also test if you find a similar result using the new PLS predict (comparing the RMSE and MAPE of the model with and without the predictor). If you find a similar result, then in fact, your model has better predictive power without the focal variable.

Does a negative Q2 also mean that you cannot/do not have to calculate effect size q2?

Thanks!

A negative Q² means that your model does not have predictive relevance. Why would you, then, compute the q² effect size? You can get the results but would it be of any use? Probably not too much.

Best regards
CR