I have a very naive question about multiple regression and errors...one that isn't addressed here already (Choosing a robust estimator to account for measurement error in dependent variable)
I would like to untangle the effect of three independent variables - P,T,S on a dependent variable CA. The problem is that I'm using pooled data across multiple experiments (not conducted by me). The experiments involve different methodologies and have introduced an un-quantified error into the measurements of CA. So, for example, even if the response to P,T,S in CA is similar across studies the values of CA might be offset between studies because of different amounts of contamination.
How do I account for this error (which is actually lab-based contamination) in the multiple regression model? Would adding the study/experiment as a categorical predictor variable help? Or is there another way?
(Note, I'm not a statistician and am not familiar with the symbology that is often used on this forum...)
Update: After some searching I'm starting to think a Random Effects Model (i.e. Proc TSCSREG in SAS) might be appropriate? Any advice would be great...
Update 2: Actually, Proc Mixed seems to do the trick...using the study as a random intercept but not as one of the effects...that is:
proc mixed data ...; class study; model ca = p t s /solution; random study; run;