I really hope you can help! I'm in the last stages of my PhD. My supervisor is keen on including all variables in the multiple regressions I am running. Some of the scales are intercorrelated (some with as high as r=.80). Is there a reason to still include them all?
I've seen some other posts and they mention multicollinearity, but if I'm looking at finding the best predictors out of a related list of possible ones (that are correlated) can I still include them all in the regression to do that? I have mainly non-significant regressions, and some significant beta values. How am I supposed to interpret them meaningfully?
From what I can interpret from other posts, it looks like I should still include them regardless and maybe discuss it after? However, if the regression isn't significant then does it just mean I can't read too much into the significant predictors? Would they still be worth looking at in the future in a follow up? I seem to have low power (n=50).
My standard errors are a maximum of .5 in some of the regression models, but usually around .2 or .3 in others. Any advice would be greatly appreciated.