Before revision:  second, for the caseswhen MCA does not show clear differences , cMCA can successfully identify meaningful directions and subgroups, which traditional methods overlook .
After revision:  second, for the caseswhen MCA does not show clear differences , cMCA can still derive latent traits that generalize across and apply to multiple groups in the dataset; finally, when data is high-dimensional and unstructured, cMCA provides objective heuristics, above and beyond the standard results, enabling more complex subgroup analysis .