Add to ORKGn this paper we present an unification to the methods for graphically summarising the association between two categorical variables that form a two-way contingency table. In particular we focus on following methods based on the decomposition of a known index: correspondence analysis (CA) based on the decomposition of Pearson’s chi-squared statistic; non symmetrical correspondence analysis (NSCA) based on the decomposition of the Goodman-Kruskal tau index; singly ordered cumulative correspondence analysis based on the decomposition of Taguchi’s statistic; doubly ordered cumulative correspondence analysis based on the decomposition of the doubly cumulative chi-squared statistic
Taguchi's statistic has long been known to be a more appropriate measure of symmetric association fo...
By using suitable parameters, we present a uni¯ed aproach for describing four methods for representi...
The generalization of simple (two-variable) correspondence analysis to more than two categorical var...
n this paper we present an unification to the methods for graphically summarising the association be...
For an analysis of the association between two categorical variables that are cross-classified to f...
Simple correspondence analysis is a very popular exploratory tool used to graphically identify the a...
Over the past few decades correspondence analysis has gained an international reputation as a powerf...
Over the past fewdecades correspondence analysis has gained an international reputation as a powerfu...
Correspondence analysis (CA) is popular method for providing a graphical summary of the association ...
The classical approach to correspondence analysis (CA) is designed to allow its user to a graphicall...
Correspondence analysis (CA) is popular method for providing a graphical summary of the association ...
A suitable measure of association for two ordered variables is the doubly cumulative chi-squared sta...
One of the most popular, and versatile, ways of visually analyzing the associating between categoric...
A suitable measure of association for two ordered variables is the doubly cumulative chi-squared sta...
Correspondence analysis is a popular statistical technique used to identify graphically the presence...
Taguchi's statistic has long been known to be a more appropriate measure of symmetric association fo...
By using suitable parameters, we present a uni¯ed aproach for describing four methods for representi...
The generalization of simple (two-variable) correspondence analysis to more than two categorical var...
n this paper we present an unification to the methods for graphically summarising the association be...
For an analysis of the association between two categorical variables that are cross-classified to f...
Simple correspondence analysis is a very popular exploratory tool used to graphically identify the a...
Over the past few decades correspondence analysis has gained an international reputation as a powerf...
Over the past fewdecades correspondence analysis has gained an international reputation as a powerfu...
Correspondence analysis (CA) is popular method for providing a graphical summary of the association ...
The classical approach to correspondence analysis (CA) is designed to allow its user to a graphicall...
Correspondence analysis (CA) is popular method for providing a graphical summary of the association ...
A suitable measure of association for two ordered variables is the doubly cumulative chi-squared sta...
One of the most popular, and versatile, ways of visually analyzing the associating between categoric...
A suitable measure of association for two ordered variables is the doubly cumulative chi-squared sta...
Correspondence analysis is a popular statistical technique used to identify graphically the presence...
Taguchi's statistic has long been known to be a more appropriate measure of symmetric association fo...
By using suitable parameters, we present a uni¯ed aproach for describing four methods for representi...
The generalization of simple (two-variable) correspondence analysis to more than two categorical var...