Multicollinearity between two categorical variables Is Variance inflation factor(VIF) also applicable in order to test multicollinearity in between two categorical variables? What is the use of the Spearman test? How to do this on R?
 A: Generalized VIF is your friend. See example:
data1<-data.frame(
  y = rnorm(8),
  x1 = factor(LETTERS[c(1,1,1,2,2,2,3,3)]),
  x2 = factor(letters[c(1,1,2,1,2,3,2,3)]),
  x3 = factor(rep(c('one','two'),4))
)

data1

            y x1 x2  x3
1 -1.20757109  A  a one
2 -0.92517490  A  a two
3 -1.97064426  A  b one
4  0.91072507  B  a two
5  0.82909639  B  b one
6  0.04714072  B  c two
7 -1.00678648  C  b one
8 -0.08177810  C  c two

library(car)

vif(lm(y~x1+x2+x3, data=data1))

       GVIF Df GVIF^(1/(2*Df))
x1 1.800000  2        1.158292
x2 4.950000  2        1.491596
x3 3.466667  1        1.861899

And read about GVIF in ?vif:

If all terms in an unweighted linear model have 1 df, then the usual
  variance-inflation factors are calculated.
If any terms in an unweighted linear model have more than 1 df, then
  generalized variance-inflation factors (Fox and Monette, 1992) are
  calculated. These are interpretable as the inflation in size of the
  confidence ellipse or ellipsoid for the coefficients of the term in
  comparison with what would be obtained for orthogonal data.
The generalized vifs are invariant with respect to the coding of the
  terms in the model (as long as the subspace of the columns of the
  model matrix pertaining to each term is invariant). To adjust for the
  dimension of the confidence ellipsoid, the function also prints
  GVIF^[1/(2*df)] where df is the degrees of freedom associated with the
  term.

