Dear researchers,
As I want to run multilevel models, I face some errors that I think originate from Grand Mean Centering or Group Mean Centering procedures. However, these two methods are somehow vague and confusing.
If my data-set has the same scale, for example, the seven-point Likert scale, Is it necessary to accomplish grand or group mean centring?
Would you please tell me when I should apply grand mean centering, and when group mean centering?
Are there any resources that teach how to accomplish these two methods in multilevel analysis?
This is a really tricky issue and no general answer can be given as to which centering procedure should be used in all cases. It depends on your specific situation. I highly recommend the chapter on this topic by Craig Enders in the SAGE handbook on multilevel modeling (2013). In this chapter, Enders nicely illustrates and compares the two approaches (both mathematically and conceptually) and provides guidelines as to when each approach may be more appropriate. I learned a lot from reading it.
Dear Dr. Christian Geiser
Thank you so much for sharing your knowledge.
Dear Saeid Nosrati
Basically, Grand mean centering subtracts the grand mean of the predictor using the mean from the full sample ( X ). Group mean centering subtracts the individual's group mean ( j X ) from the individual's score.
This is a fine tutorial on this concept, have a look:
https://www.youtube.com/watch?v=NSFCibkocRA
Best Regards
Dr. Fatemeh Khozaei
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