Abstract
We consider a bivariate normal distribution with linear correlation $$\rho $$
ρ
whose random components are discretized according to two assigned sets of thresholds. On the resulting bivariate ordinal random variable, one can compute Goodman and Kruskal’s gamma coefficient, $$\gamma $$
γ
, which is a common measure of ordinal association. Given the known analytical monotonic relationship between Pearson’s $$\rho $$
ρ
and Kendall’s rank correlation $$\tau $$
τ
for the bivariate normal distribution, and since in the continuous case, Kendall’s $$\tau $$
τ
coincides with Goodman and Kruskal’s $$\gamma $$
γ
, the change of this association measure before and after discretization is worth studying. We consider several experimental settings obtained by varying the two sets of thresholds, or, equivalently, the marginal distributions of the final ordinal variables. This study, confirming previous findings, shows how the gamma coefficient is always larger in absolute value than Kendall’s rank correlation; this discrepancy lessens when the number of categories increases or, given the same number of categories, when using equally probable categories. Based on these results, a proposal is suggested to build a bivariate ordinal variable with assigned margins and Goodman and Kruskal’s $$\gamma $$
γ
by ordinalizing a bivariate normal distribution. Illustrative examples employing artificial and real data are provided.
A recommendation for applied researchers to substantiate the claim that ordinal variables are the product of underlying bivariate normal distributions
