This　paper　presents　a　simplified　and　computationally　feasible　multivariate　extension.　A　correlation　matrix　is　constructed　using　pairwise　Spearman＇s　footrule　correlation　coefficients,　and　these　coefficients　are　shown　to　jointly　converge　to　a　multivariate　normal　distribution.　A　global　test　statistic　based　on　the　Frobenius　norm　of　this　matrix　asymptotically　follows　a　weighted　sum　of　chi-square　distributions.　Simulation　studies　and　two　real-world　applications　(a　sensory　analysis　of　French　Jura　wines　and　the　characterization　of　plant　leaf　specimens)　demonstrate　the　practical　utility　of　the　proposed　method,　bridging　the　gap　between　theoretical　rigor　and　practical　implementation　in　multivariate　nonparametric　inference.