By Terry Allen, Amber Thom, and Glen Buckner

Multiple correspondence analysis (MCA) is a highly useful descriptive statistical technique that creates dimensional coordinates used to graphically display relationships among the attributes of multiple variables. It calculates row and column coordinates that are analogous to factors in a principal component analysis (PCA), differing from the latter in that it partitions the chi-square value instead of the total variance among variables.

Although MCA is an excellent technique for examining large numbers of categorical variables, it has received little attention in social science research. Many social research variables are naturally discrete, as they are measured at the nominal level. Such variables are naturally displayed in contingency tables and well suited for analyses using the Pearson chi-square or the likelihood ratio chi-square, methods frequently used by social researchers.

Some content is only viewable by ASA Members. Please login or become an ASA member to gain access.