Image: Affinity Diagram with several levels of groupings in a hierarchy
Grouping sticky notes aka affinity diagramming is a method for analyzing comments, ideas, quotes, observations etc. (aka qualitative data) by clustering similar data (which is on sticky notes) and giving these clusters of data a summarizing title.
I noted that I, as well as many fellow users of the method have the tendency to use just a single word on the note that summarizes the theme of a group. But often, I think, we could do better and create a more helpful analysis by going beyond single words.
Why are single words often not useful?
Single Words are often Vague
What does a theme like »Empathy« tell you? Not much. For two reasons:
- Empathy is a rather abstract word.
- What about Empathy? If could be something that lacks, something that is there and to be celebrated or something that matters in particular situations.
If you have interesting, rich data, it is not served well by a vague and nondescript theme. Themes on Emotions, Needs and Values are particularly prone to this.
Throw it in the Box with [Word]!
In contrast, if your theme deals more with concrete things, workflows and observations you might end up with a theme that is a mere label on a box you throw your data into whenever the a thing is mentioned. Like »Bicycles« as a theme. In the long version this is: »Everything where Bicycles were used or mentioned«.
This is not entirely useless: It helps you to find Quotes and Observations. But that is it. It does not abstract the underlying data, it does not give you any new insights.
Write short assertions instead
Instead of a single word you can write a short assertion which is supported by the data in the concerning cluster. Maybe the group formerly called »bicycle« is actually about »bicycles are useful«, stating that they are (also) seen as a tool. Maybe this cluster has sub-clusters, like »bikes are fast in the city« and »bikes can transport small amounts easily«
In contrast to a single word, assertions allow you to check:
- If data fits them, which means if a piece of data should go into that cluster or not
- If an idea fits them, helping you to evaluate it in the context of your research
So: Grab your sticky notes, maybe even the larger ones and try to group your data according to the assertions they support.
Finding Group Titles in Sticky Notes Clustering by Jan Dittrich is licensed under a Creative Commons Attribution 4.0 International License.