# Statistics - Multivariate Exploratory Techniques - Correspondence Analysis

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Mult/Exploratory and on the menu, select Correspondence to display the Correspondence Analysis (CA): Table Specifications Startup Panel.

Classic menus. On the Statistics - Multivariate Exploratory Techniques submenu, select Correspondence Analysis to display the Correspondence Analysis (CA): Table Specifications Startup Panel.

Correspondence analysis provides a descriptive/exploratory technique designed to analyze simple two-way and multi-way tables containing some measure of correspondence between the rows and columns. The results provide information that is similar in nature to those produced by factor analysis techniques, and they allow you to explore the structure of categorical variables included in the table.

The Correspondence Analysis module features a full implementation of simple and multiple correspondence analysis techniques. STATISTICA accepts input data files with grouping (coding) variables that are to be used to compute the crosstabulation table, data files that contain frequencies (or some other measure of correspondence, association, similarity, confusion, etc.) and coding variables that identify (enumerate) the cells in the input table, or data files with frequencies (or other measure of correspondence) only (e.g., you can directly type in and analyze a frequency table). For multiple correspondence analysis, you can also directly specify a Burt table as input for the analysis. STATISTICA computes various tables, including the table of row percentages, column percentages, total percentages, expected values, observed minus expected values, standardized deviates, and contributions to the Chi-square values. Results include the generalized eigenvalues and eigenvectors, all standard diagnostics including the singular values, proportions of inertia for each dimension, standard coordinate values for column and row points, etc. In addition to the 3D histograms that can be computed for all tables, you can produce a line plot for the eigenvalues, and 1D, 2D, and 3D plots for the row or column points. Row and column points can also be combined in a single graph, along with any supplementary points (each type of point will use a different color and point marker, so the different types of points can easily be identified in the plots).

To analyze the structure (dimensions) of variables in a correlation or covariance matrix, and to apply that structure to supplementary variables and observations, you can also use the Principal Components and Classification Analysis (PCCA) module.