Card Sorting Analysis

This post may seem out of context but it may be useful for someone out there. I will be speaking about Card Sorting analysis.

The technique is well described in a lot of websites and books, but it mainly consists in a low-fi usability method used to organize information. Do you want to organize a menu for a website? Write the names of all pages in some cards, ask some people to organize them according to some criteria, or according to predefined categories, an finally ask them to give a name to the created categories. You’ll end up with different proposals of organization for the website menu. And then?

These are just some ideas for the data analysis, if someone finds it useful I could develop some a bit more.
(Before I forget, SynCaps software is a really really cool for card sorting analysis).

1. Consensus Analysis

To begin with, you can see if your participants differ in someway. Imagine you gather participants from different backgrounds, their mental models can be different, and that can distort your final analysis.
This type of analysis is used to see the degree of agreement between participants, and it uses the participants matrix. The output is the percentage of consensus between participants, after the calculation of the number of cards that would have to be moved to have Participant A having the same organization as Participant B. The higher this percentage, the more the consensus among these two participants.

2. Item X Item

This is the classic analysis where the number of times an item is paired with another is organized in a matrix.

3. Item X Group

Same as the previous analysis, except it represents the strength with which each item was associated with a group.

4. Cue Validity

It could be interesting if we could know the “findability” of an item. That is, if we were looking specifically for it, would we search it in the right place? Some authors suggest that this findability is correlated with the category validity – the frequency of an item within its category and its proportional frequency in that category compared to all other categories. The cue validity index was created to evaluate this findability. It is the frequency with which an item is associated with the category in question, divided by the total frequency os that item over all categories. The Item X Item matrix is used for this, and when the index reaches 1.0, it means it was never grouped in any of the other categories by all participants.

5. Contextual Navigation

When you have items with low cue validity/ findability, one solution could be Contextual Navigation. This strategy creates links to a particular page, like the “see also” feature often seen in e-commerce websites. One common example is Amazon’s “Customers Who Bought This Item Also Bought”.
This is done by extracting the row of the item X item matrix of that item with low cue validity. After, all elements should be ordered by the number of times they have been classified together with the target item. The frequencies belonging to the same category are removed, and then a number of selected items is chosen by the designer (eg. the three more similar).

I believe these analysis will give you already useful hints on the number of categories you should create, if some attention should be paid to differences in groups of participants and how/where to place the difficult items.

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