Topic Correlations & Correlation Explanation

Understand how Caplena measures the strength of relationships between topics, and how to interpret the results.

The Topic Correlations view helps you discover which themes in your data tend to appear together, and the Correlation Explanation shows why they’re connected.

What It Shows

Caplena automatically detects relationships between topics that are frequently mentioned in the same responses. Each row in the table represents a pair of correlated topics, showing how strongly they are linked. The correlation score (r) tells you the strength of the relationship — higher values mean the topics co-occur more often.

Example: Audio Quality ↔ Visual Quality (r = 0.53) — respondents who mention sound quality also often talk about picture quality.

Switch Between Views

On the right-hand side, you can explore your correlations in two different views:

Chord Diagram

A visual map showing how your topics connect.

• Each bubble represents a topic.
• Lines show connections between topics — the thicker the line, the stronger the correlation.
• Bubble size reflects how frequently a topic appears in your dataset.

Hover over a topic to highlight its links, or click to explore specific relationships.

Correlation Explanation

When you switch to this view, Caplena’s AI provides a written summary explaining the connection between the selected topics.

How the Correlation Metric Is Calculated

The correlation score is based on the Kulczyński measure (pronounced Kool-chin-ski). This score ranges from 0 to 1 and shows how likely two topics are to appear together.

• A value close to 1 means the topics are strongly related.
• A value close to 0 means they rarely appear together.

Unlike simpler methods, this measure accounts for how often each topic appears individually — so it works well even when one topic is far more common than the other.

The Idea Behind It

The Kulczyński score asks: “If I see Topic A, how often do I also see Topic B?” — and vice versa — then takes the average of both directions.

That’s why it’s great for spotting balanced, meaningful relationships rather than random overlaps.

Formula:

0.5 * (A / (A + B) + A / (A + C))

Where:

• A = number of responses mentioning both topics
• B = number of responses mentioning only Topic 1
• C = number of responses mentioning only Topic 2

Example calculation:

To calculate the Kulczyński value between Design and Product:

• Design and Product mentioned together: A = 100
• Design mentioned alone (without Product): B = 20
• Product mentioned alone (without Design): C = 80

0.5 * (100 / 120 + 100 / 180) = 0.7

More Examples

Topic A Mentions	Topic B Mentions	Both Together	Kulczyński	What It Means
100	100	50	0.50	Appear together half the time — moderately related.
100	1	1	0.505	Topic B is rare, but always appears with A — noticeable link.
50	100	50	0.75	Every time A appears, it’s with B — strong connection.