Correspondence analysis is useful when you have a table with at least two rows and two columns, no missing data, no negative values, and all the data has the same scale. The only hard bit of this to understand is "same scale", which is the focus of the examples here.

How is correspondence analysis useful in research?

Correspondence analysis offers a potential means for communication researchers to examine, and better understand, relationships between categorical variables. Though traditionally not commonly used in communication research, potential applications for CA exist.

Why is correspondence analysis important?

The utility of correspondence analysis lies in displaying such patterns for two-way tables of any size. If there is an association between the row and column variables--that is, if the chi-square value is significant--correspondence analysis may help reveal the nature of the relationship.