Magnitude of standardized coefficients (beta) in multiple linear regression Being aware of that article, I am curious about the question how big standardized coefficients can get. I had a discussion with my professor about that issue and she was arguing standardized coefficients (beta) in multiple linear regressions can not become greater than |1|. I have also heard that predictors with standardized coefficients greater than 1 should not be be included/appear in multiple linear regression. When I recently estimated a multiple linear regression in R using lm(), I estimated the standardized coefficients with lm.beta() function from the package 'lm.beta'. In the results I could observe a standardized coefficient greater than one. Right now I am just not sure about what is the truth.
Can standardized coefficients become greater than |1|?
If yes, what does that mean and should they be excluded from the model?
If yes, why?
I would be very thankful, if somebody could make this issue clear for me. 
 A: This is probably a matter of definitions.  Does a standardized coefficient refer to standardizing only the predictor variables? or standardizing the response variable as well?   I have seen both used to compute "standardized coefficients".  Even then, there is more than one way to standardize.
If you divide both the predictor and response variable by their standard deviations (common way to standardize) and fit the regression (with only a single predictor/a single slope coefficient) then it is mathematically impossible to see a coefficient outside of the -1 to 1 range (since the slope will be the same as the correlation).  But if you don't standardize the response variable then it would be easy to see an estimated coefficient outside of -1 to 1 depending on the scale of the response variable.
With multiple predictors, an unusually large standardized coefficient could be a sign of multi-colinearity and is probably why some sources suggest dropping those variables.
I expect that the differences between what some sources say are possible and what you observe from others is due to the difference in definitions.
A: A standardized beta weight greater than one is a sign of suppression, especially cooperative suppression.  Such suppression increases the predictive value of the predictors and thus is of potentially great value.  See http://core.ecu.edu/psyc/wuenschk/MV/multReg/Suppress.docx
