On your data

For non-spatial data

If you're not interested in how your variables look as an overall space, CorrExplore can also help you:

Explore the numerous possible correlations in your data to get an overview and find questions you didn't know to ask.
Find relationships changing over time.
Find relationships that differ across segments of the data such as demographics.
Find relationships that differ across experimental conditions.
Find differences between the items in your scales in how they relate to other variables.

What you put in

Any quantitative variables you want, measured on the same set of people. For example, a table with a column for each question asked of a group of people, and a row for each person's answers.

What it communicates

Although in general variables closer together will tend to be related, all maps are necessarily imperfect representations of correlation networks. This is why CorrExplore's interactive features are important. These features are designed to communicate the correlation network instead of oversimplifying it like cluster analysis, factor analysis, or other mapping techniques that produce still images. For example, still images can't effectively communicate all ties because this would make the display too cluttered; in CorrExplore, you can hover your mouse to reveal correlations for each variable.

Visually explore correlation networks