What it is
Correspondence analysis is a dimensionality-reduction approach used to visualize association patterns in categorical data.
Overview notes
Common risk
The chart often looks more precise than it really is. Interpretation discipline matters.
Decision guide
When to use it
- When a perceptual map would help tell the story
- When the data expresses associations between sets of categories
When not to use it
- When stakeholders may over-read distance and causality
Inputs required
- A suitable contingency or association table
Typical outputs
- Two-dimensional map
Simple example
Map brands against key associations to identify white space and crowded territory.
Strengths
- Visually efficient
Limitations
- Easy to misread if context is weak
Common mistakes
- Over-interpreting small spatial gaps
How I use it in practice
I use it when a spatial view helps synthesize brand or category association data into a clearer strategic discussion.
What is outputted
- Perceptual map
How to interpret the output
- Read proximity directionally and with caution
How to communicate to clients
- Walk through what the map represents before drawing implications
Displayr / Q implementation notes
- Label outputs carefully and avoid cluttered plots
Visual placeholder
Perceptual map placeholder
Add a brand-attribute map screenshot with one or two interpretation callouts.
Recommended placeholder: chart screenshot, process diagram, output interpretation notes, and one short caption on what to inspect first.