Abstract
Multiple correspondence analysis (MCA) is a useful tool for exploring the interdependencies among multiple-choice variables. However, MCA is not geared for explicitly investigating whether or not heterogeneous subgroups of respondents exist in the population with qualitatively distinct patterns of choice behaviour. In this paper, we extend MCA to capture such cluster-level heterogeneity. Specifically, the proposed method combines MCA with fuzzy k-means simultaneously. Consequently, it can provide a single map of displaying variable-level and cluster-level structures so as to facilitate the interpretation of the underlying structures. The performance of the proposed method in recovering true coordinates is investigated based on a Monte Carlo study involving synthetic data. In addition, two empirical applications are presented which compare the proposed method to two extant approaches that combine MCA and cluster analysis.
