A dendrogram is a type of diagram used to represent hierarchical clustering of objects or data points – it’s a way to quickly spot popular groups of cards, and to get a general sense of how similar (or different) your participants’ card sorts were to each other.
You have two dendrograms to explore, which you’ll find more or less useful depending on the number of completed card sorts you have.
The AAM dendrogram is best for studies that have more than 30 participants. It depicts only factual relationships – it shows the percentage of participants who agree with the grouping of the cards.
Below you’ll see an example of a study conducted with 72 participants, where we asked them to group items they’d find on a department store website. As you hover and move the mouse along, you can see the different agreement scores for the card grouping.
We can see that 74% of participants agree that Backpack and Duffle Bag belong together in either the “Bags” or “Travel” category – these are labels created by the participants – or by you when you standardized – who grouped those cards together (they’re just suggestions, but are more accurate the higher the agreement).
This data gives us ideas for how we could group and label that particular content on our website.
Moving out, we can see that 37% of participants agreed that Cooler/Lunch Box, Water Bottle, Backpack, and Duffle Bag belonged together in the categories “On the go”, “Bag to go”, or “Bags”. so , there is less agreement, less surety. Maybe there is a better name you can think of that might aid agreement? Maybe if you remove something, there would be more agreement? What’s the thing that’s less congruent? Think about that.
The BMM dendrogram is better for studies with smaller participant numbers (usually less than 30). Unlike the AAM which shows only factual relationships, the BMM makes assumptions about larger clusters based on pair relationships, and tells you the percentage of participants that agree with parts of the grouping. Thus, if I grouped six cards together and you grouped five of them plus another, BMM might say ‘close enough’ and represent these as all grouped together. The algorithm is filling in for the limited number of respondents by making assumptions about clusters.
Essentially, by compromising and extrapolating on the data, it helps you squeeze the most out of small or incomplete responses.
Other than being more appropriate for smaller numbers of responses, the process of using the BMM dendrogram is otherwise the same.