P1: Research Methods for Information Architecture 1


Information architecture (IA) is the practice of organizing, structuring, and labeling content within websites, intranets, and other digital platforms to help users find and navigate through information efficiently. It plays a crucial role in creating user-friendly, accessible, and efficient digital spaces that facilitate effective communication and interaction. As technology continues to advance and user expectations evolve, understanding and implementing the principles of IA and employing research methods to evaluate and improve it becomes increasingly important.

Some Factors Used in IA Design

1. Gestalt Principles

Gestalt principles, derived from the field of psychology, inform the way human brains perceive and interpret visual elements. These principles can be applied to IA design to create intuitive and visually coherent interfaces. Key Gestalt principles include:

  • Proximity: Elements that are close together are perceived as related.
  • Similarity: Elements that share visual characteristics are perceived as related.
  • Continuity: The human eye follows continuous lines or paths.
  • Closure: The brain tends to complete incomplete shapes or forms.
  • Figure-Ground: The brain distinguishes between objects and their backgrounds.
  • Accounting for Mental Models

Mental models are the cognitive representations of how users perceive, understand, and predict the functioning of a system. IA design should consider these mental models to create intuitive and user-friendly interfaces. By aligning the IA with users’ expectations, designers can reduce learning curves and ensure smooth navigation.

  • Cognitive Load

Cognitive load refers to the amount of mental effort required to process information. IA design should minimize cognitive load by organizing and presenting information in a clear, logical, and easily digestible manner. Strategies for reducing cognitive load include chunking information, using visual hierarchy, and employing clear navigation elements.

Purpose of IA

Navigation is the visible aspect of IA, enabling users to move through digital spaces. However, IA, the backbone of navigation, is mostly hidden, as it involves the underlying organization and structure of the content. The metaphor of an iceberg illustrates this distinction: what users see (navigation) is only the tip of the iceberg, while the rest (IA) lies beneath the surface. Good IA ensures seamless navigation and user experience, without drawing attention to itself.

Findability and Discoverability

Findability refers to how easily users can locate content within a system. Effective IA design enables users to quickly find the information they are looking for. Discoverability, on the other hand, is the ability of users to notice and explore new content. Balancing findability and discoverability in IA design enhances user engagement and satisfaction.

Research Methods for IA

Card Sorting

Card sorting is a generative and exploratory research method that involves users organizing and categorizing content into groups. This technique helps designers understand users’ mental models and uncover optimal content organization patterns. There are three types of card sorting:

  • Open Card Sorting: Users create and name their own categories.
  • Closed Card Sorting: Users sort content into predefined categories.
  • Hybrid Card Sorting: A combination of open and closed methods.

Card sorting results are analyzed by examining category patterns, identifying common themes, and understanding the rationale behind users’ decisions. Designers can use these insights to inform IA design and improve findability and discoverability.

Tree Testing

Tree testing is an evaluative method that assesses the effectiveness of an IA by asking users to locate specific items within a hierarchical content structure, without visual design elements. This technique helps designers identify problems with labeling, categorization, and structure.

The results of tree testing can reveal the success rates of locating content, the time taken to complete tasks, and the paths users take to find information. These insights inform designers on how to improve IA, ensuring a more efficient and user -friendly experience.

The process of tree testing includes the following steps:

  • Define tasks: Develop tasks that require users to find specific content within the IA.
  • Create a tree: Design a simplified, text-based hierarchy of the IA, stripped of visual elements.
  • Recruit participants: Select a diverse group of users representative of your target audience.
  • Conduct the test: Have participants complete the tasks using the tree, while recording their paths, success rates, and time taken.
  • Analyze results: Identify patterns and trends in the data to understand where users struggle or succeed in finding content.
  • Iterate and refine: Use insights from the tree testing analysis to make informed changes to the IA and repeat the process until satisfactory results are achieved.

Tree testing is particularly useful during the early stages of IA development, as it allows designers to evaluate and optimize the structure and labeling of content without investing in visual design. It can also be employed to assess the effectiveness of existing IA and identify areas for improvement.


Information architecture is fundamental to creating user-friendly, efficient, and accessible digital spaces. By understanding and applying principles such as Gestalt psychology, mental models, and cognitive load, designers can develop intuitive and engaging interfaces. Research methods like card sorting and tree testing provide valuable insights into users’ mental models and the effectiveness of IA design, enabling iterative improvements that enhance findability, discoverability, and overall user experience.

The principles and research methods discussed above were created for the era of static information architecture.  The recent advances in AI requires us to make fundamental shifts in our approach to AI.  You can find some thoughts on that in part 2 of this article.

About Vivek Srinivasan

I work with the Program on Liberation Technology at Stanford University. Before this, I worked with the Right to Food Campaign and other rights based campaigns in India. To learn more, click here.

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