  Square contingency tables are a special case commonly used in various fields
to analyze categorical data. Although several analysis methods have been
developed to examine marginal homogeneity (MH) in these tables, existing
measures are single-summary ones. To date, a visualization approach has yet to
be proposed to intuitively depict the results of MH analysis. Current measures
used to assess the degree of departure from MH are based on entropy such as the
Kullback-Leibler divergence and do not satisfy distance postulates. Hence, the
current measures are not conducive to visualization. Herein we present a
measure utilizing the Matusita distance and introduce a visualization technique
that employs sub-measures of categorical data. Through multiple examples, we
demonstrate the meaningfulness of our visualization approach and validate its
usefulness to provide insightful interpretations.
