Multiple Correspondence Analysis

Abstract

This chapter examines biplots for more than two categorical variables. One way of generalizing correspondence analysis (CA) is to treat the categorical data matrix G as if it were a two-way contingency table. Although the chi-squared distances based on the Burt matrix are functions of those of the correspondence analysis of a contingency table, they differ from the row chi-squared distances used in the analysis of G. Gower and Hand suggested the extended matching coefficient (EMC) which expresses the number of matches for every pair of samples as a ratio of the number p of variables. Category-level points have been used extensively above. The chapter supplies an amplified discussion and an introduction to the concept of prediction regions. In homogeneity analysis the chapter seeks scores z = (z1, z2, . . . , zp ), often termed quantifications, that replace G by Gz. Controlled Vocabulary Terms Chi-square test for homogeneity; correspondence analysis; two-way tables