Question 1

What is card sorting?

Accepted Answer

Card sorting is a user research technique used to gather insights into how users organize information. Participants are presented with a set of cards containing information or items and asked to sort them into groups that make sense to them.

Question 2

What are the benefits of card sorting?

Accepted Answer

Card sorting can help you create a more effective information architecture by identifying how users think about and organize information. This can lead to clearer and more intuitive navigation, increased user engagement, and reduced development costs.

Question 3

How do I choose the right card sorting method?

Accepted Answer

Lyssna offers three types of card sorting: open, closed, and hybrid.

Open card sorting allows participants to create their own groups and labels, making it useful for exploring how users naturally organize information.

Closed card sorting uses predefined categories, which is helpful for validating an existing structure.

Hybrid card sorting combines both approaches, allowing participants to sort into predefined categories or create their own. This can be useful when you want to validate your structure while still leaving room for new insights.

The method you choose depends on your research goals and whether you're exploring, validating, or doing a bit of both.

Question 4

How many participants do I need for a card sorting study?

Accepted Answer

The number of participants you need for a card sorting study depends on the complexity of the information and the level of detail you want to achieve. Typically, a sample size of 15–20 participants is recommended. You can use our sample size calculator to find out more here.

Question 5

How do I analyze the results of a card sorting exercise?

Accepted Answer

There are various methods for analyzing the results of a card sorting exercise. Lyssna offers a variety of built-in data analysis features to simplify the process of analyzing your results. You can view the results in real-time, including participant responses and groupings, and visualize the data using the agreement matrix or similarity matrix. You can also automatically group and filter results.

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