Card Sorting
Last updated
Last updated
Card sorting is a qualitative UX research method used to understand how users categorize and organize information. It involves participants sorting labeled cards into groups based on their understanding and preferences. This process reveals insights into users' mental models and expectations regarding how information should be structured and accessed on a website or application.
Card sorting is a method that helps uncover how individuals think, allowing you to organize content in a manner that resonates with them. It's not just about structuring a website or application; it delves into understanding people's perspectives and the reasoning behind their categorizations. This process reveals insights into how personal backgrounds and experiences influence cognitive processes.
Successful card sorting sessions provide several key learnings:
Grouping Logic: It shows what individuals perceive as belonging together and the rationale behind their groupings.
Organizational Methods: Reveals different approaches and ideas for organizing content effectively.
Diverse Perspectives: Highlights whether users think similarly or differently about categorization.
While card sorting is commonly used for developing information architectures, website navigation, and menu structures, its application extends to internal communication and decision-making processes within organizations.
However, it's important to note that while card sorting can significantly enhance your information architecture (IA), it's not a definitive tool for creating one from scratch. Due to its qualitative and exploratory nature, results can vary, making it challenging to rely solely on for establishing a robust IA. For assessing the effectiveness of an IA, Donna Spencer recommends complementing card sorting with a tree test.
Card sorting represents a versatile UX research technique with several types tailored to different needs. Each type provides distinct insights into how users organize and comprehend information. Let's delve into these card sorting methods with detailed explanations and examples for each.
Open card sorting involves participants sorting cards into categories they create themselves. This method is ideal for understanding how users naturally group information without imposed categories.
Example: Imagine receiving a set of cards on a music streaming app labeled with genres, artists, and songs. Users might create categories such as Workout Music, Relaxing Tunes, or Party Hits, revealing their preferences for playlist organization.
Closed card sorting requires participants to sort cards into predefined categories provided by researchers. It's useful for testing specific groupings or evaluating an existing structure.
Example: In an online bookstore scenario, users receive cards with book titles and categorize them into predefined groups like Fiction, Non-fiction, Biography, and Children’s books. This helps refine the bookstore's current categorization system.
Hybrid card sorting combines elements of both open and closed card sorting. Participants sort cards into provided categories while also having the flexibility to create new categories. This method offers insights into user preferences with a degree of structure.
Example: On a travel website, users categorize destinations and activities into predefined groups like Beaches, Mountains, and Cities. They can also introduce new categories like Adventure Travel or Family-friendly, providing nuanced insights into travel preferences.
Reverse card sorting, or tree testing, involves users determining where a card should fit within an existing structure, testing the usability of site navigation. Results indicate how intuitively users perceive the organization of information.
Example: On a cooking website, users are given specific recipe cards like Vegetarian Lasagna and must place them within pre-established categories such as Italian Cuisine, Vegetarian Dishes, or Pasta Recipes. This evaluates the effectiveness of the website's navigation system.
The Modified-Delphi method evolves iteratively with each participant. Initially, one participant organizes items into categories based on their understanding. Subsequent participants refine this model collectively, progressively enhancing categorization based on consensus.
Example: A team developing a health app categorizes features such as Exercise Tracking, Diet Plans, and Mental Wellness. Each participant categorizes these features independently, followed by group discussion to refine and consolidate categories.
Remote card sorting enables participants to sort cards using online tools, making it convenient for a diverse and geographically dispersed audience to participate in UX research activities.
Example: An e-commerce platform utilizes an online tool where users categorize product types such as Electronics, Home Goods, and Fashion. Participants complete the sorting task remotely on their computers, providing valuable insights without requiring physical presence.
These methods illustrate the versatility of card sorting in UX research, offering tailored approaches to understanding user mental models and preferences in information organization.
Ease of Execution: Few techniques are as straightforward as handing someone cards and asking them to organize them.
Cost-Effectiveness: Requires minimal materials such as cards, ink, and possibly sticky notes or tape.
Speed: Can be conducted rapidly and repeated as needed to gather sufficient data.
User-Centric: Focuses on user perspectives, prioritizing their input over assumptions for better product alignment.
Familiarity: Users easily understand the concept, requiring little explanation due to its historical use.
Insightful: Provides valuable insights into how users naturally categorize information, aiding in design decisions.
Task Relevance: May not always translate practical implications effectively into product features or functions.
Inconsistency: Results can vary significantly due to individual differences in perception and categorization.
Analysis Time: While quick to perform, analyzing results, especially with complex data sets, can be time-consuming.
Surface-Level Insights: Provides insights mainly at a high-level without delving deeply into underlying issues or contexts.
