“Correspondence analysis provides tools for analyzing the associations between rows and columns of contingency tables. A contingency table is a two-entry frequency table where the joint frequencies of two qualitative variables are reported. For instance a (2 x 2) table could be formed by observing from a sample of n individuals two qualitative variables: the individual’s sex and whether the individual smokes. The table reports the observed joint frequencies. In general (n x p) tables may be considered.

“The main idea of correspondence analysis is to develop simple indices that will show the relations between the row and the column categories. These indices will tell us simultaneously which column categories have more weight in a row category and vice-versa. Correspondence analysis is also related to the issue of reducing the dimension of the table…” (p. 341)

Applied Multivariate Statistical Analysis

“A frequently applied paradigm in analyzing data from multivariate observations is to model the relevant information (represented in a multivariate variable X) as coming from a limited number of latent factors. In a survey on household consumption, for example, the consumption levels, X, of p different goods during one month could be observed. The variations and covariations of the p components of X throughut the survey might in fact be explained by two or three main social behavioral factors of the household…These unobserved factors are much more interesting to the social scientist than the observed quantitative measures (X) themselves, because they give a better understanding of the behavior of households.

“How can we provide a statistical model addressing these issues and how can we interpret the obtained model? This is the aim of factor analysis.” (p. 276)

Applied Multivariate Statistical Analysis