Hi folks
A regression question - perhaps better placed on a different forum, but I thought I'd try here first :)
Partial correlations allow one to assess the unique contribution of a given set of predictors to the variance in Y outcome.
Let's say predictors x1, x2, x4, and x5 all stuck in a regression model accounted for 30% variance in Y outcome. Then you conduct partial correlations to assess their unique effects. Would it be possible for some of those predictors (let's say x1 and x2) to cause no unique variance? (i.e., weak and non significant correlations to Y outcome) - even though they were significant predictors in the overall model?
I think so but I am driving myself a bit mad!
Hopefully someone will be able to answer! : )
Thanks pd1598! Very helpful responses (in bite size chunks as well!). I haven't found this with my data, but I came across some results in a paper that made me question it... Quite an interesting notion really if it is the case. I mean, it would mean that if you had not included some other relevant predictors in your model, it may appear that a given set of predictors are accounting for the variance in the outcome. When actually they only appear to because they are correlated with some more important variables that you have missed. Do you see what I mean?
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