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The practical guide behind this tool: when it applies, how to read the output, and the traps that make it say the wrong thing.
Standard-library analysis: how do two categorical variables map together? Map two categorical columns (brands against attributes, segments against behaviours, products against complaint reasons) and get the classical correspondence analysis: the contingency table's total inertia with a chi-square test of independence, the dimension table showing how much of the association each dimension explains, row and column coordinates on the first two dimensions, the symmetric biplot with both sets of categories on one map, and per-category contribution and quality-of-representation (cos-squared) tables so you know which points are actually well enough represented to interpret. The report states, in your own column names, the rule almost every reader breaks: in a symmetric biplot the distance from a row point to a column point is not a measure of association.
Interactive scatter visualization
Interactive bar visualization
Interactive table visualization
Interactive table visualization
Interactive table visualization
Plain-English reading of the results and what to do next.
Map brands against the attributes people associate with them
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MCP Analytics. Correspondence Analysis. Free tool. https://mcpanalytics.ai/free/standard_correspondence. Accessed [date you used it].
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