Skip to main content
All CollectionsOptimalSortAnalyze your OptimalSort results
Interpret the participant-centric analysis (PCA)
Interpret the participant-centric analysis (PCA)

Learn how to read the participant-centric analysis visualization to help you see the three most popular participant IAs.

Updated over a week ago

The PCA shows you the three most popular participant IAs (completed card sorts) based on how often 2 cards are paired together in the same category throughout the whole study. This information can be a useful starting place when you're starting to draft a few potential website structures.

It's called a 'Participant-centric analysis' because every participant IA is treated as a vote towards the IA it supports the most.

Keep in mind, the data included in the PCA is only that of your completed participants. If you have included some abandoned participants on your participants tab, they won't be included in this analysis.

Here's how it works:

If two participant IAs contain 50% or more of the same card pairings — that is, 2 cards appearing together in any category — then the IAs 'support' each other.

The PCA doesn't take category labels into account when determining similarity, though it does analyze them separately, and offers suggestions based on the category labels of similar IAs.

In the example below, our first IA on the left has 37/50 similar IAs, which means 37 participants out of a total of 50 paired the same cards together at least 50% of the time. The PCA is showing us this particular IA because it's the one with the most support from the other 36 participants. We could comfortably base our initial draft IAs for our website on a PCA result with this level of agreement.

The other 2 IAs displayed operate in the same way. The 3 are distinct from each other because they don't support each other (as in, fewer than 50% of card pairings in one IA matches the card pairings in the other 2).

What happens if you see low levels of agreement

If you see low levels of agreement for the three IAs (for example, 1/15 participant sorts were similar to this IA) this shows that none of the participants’ sorts are similar to each other. That is, each of the 15 participants have come up with different categories and grouped their cards in different ways.

To address this, you can recruit more participants to get a better sample size and see if more people come up with similar card sorts. Otherwise, you can focus your analysis on the other visualizations, like the Similarity Matrix and the Dendrogram (actual agreement method).

Did this answer your question?