What I want to do is to construct GLMM’s to evaluate resource selection, and I have a set of variables (some representing distances and others representing % of land cover).

Can I test for correlation between variables before standardize them? I am not quite sure what should I do first.

**Answer**

Can I test for correlation between variables before standardize them? I am not quite sure what should I do first.

Correlation will be the same regardless whether you calculate it before or after standardization. To see this, it is enough to know that correlation is invariant to scale. Take b∈R and a>0, then

Corr(aX−b,Y)=Cov(aX−b,Y)√Var(aX−b)√(Var(Y)=Cov(aX,Y)√Var(aX)√Var(Y)=aCov(X,Y)√a2Var(X)√Var(Y)=aCov(X,Y)a√Var(X)√Var(Y)=Cov(X,Y)√Var(X)√Var(Y)=Corr(X,Y)

The first equality is a definition.

The second uses the property that covariance as well as variance are invariant to location shifts.

The third uses the properties of covariance and variance with respect to multiplication by a constant.

The fourth uses the fact that a>0.

The fifth just cancels out the multipliers.

The sixth is again a definition.

This covers standardization, which is subtracting the mean and dividing by the standard deviation (a positive number).

**Attribution***Source : Link , Question Author : mtao , Answer Author : Richard Hardy*