Correspondence Analysis for Symbolic Multi--Valued Variables

TL;DR

This paper introduces a novel method and algorithms for applying Correspondence Analysis to symbolic multi-valued variables, enabling analysis of complex questionnaire data with multiple selections.

Contribution

It proposes a new approach and algorithms for extending Correspondence Analysis to symbolic multi-valued variables, addressing a gap in analyzing multi-selection questionnaire data.

Findings

Developed a new method for SymCA using interval contingency tables

Created two algorithms for implementing the proposed method

Facilitated analysis of multi-valued symbolic data in questionnaires

Abstract

This paper sets a proposal of a new method and two new algorithms for Correspondence Analysis when we have Symbolic Multi--Valued Variables (SymCA). In our method, there are two multi--valued variables $X$ and $Y$, that is to say, the modality that takes the variables for a given individual is a finite set formed by the possible modalities taken for the variables in a given individual, that which allows to apply the Correspondence Analysis to multiple selection questionnaires. Then, starting from all the possible classic contingency tables an interval contingency table can be built, which will be the point of departure of the proposed method.