Which method can be used to categorize and analyze large amounts of user data in UX research?

The most effective method for categorizing and analyzing large amounts of user data in UX research is the Affinity Diagram. This technique is especially useful when researchers have collected substantial qualitative data, such as insights from user interviews, surveys, or usability tests.

When utilizing an Affinity Diagram, researchers can organize the data into groups or themes based on their relationships and commonalities. This can reveal patterns or trends that may not be immediately obvious, helping to clarify user needs and behaviors. The visual representation of data in this manner allows teams to easily share findings and foster collaboration during the analysis process.

Other options, while valuable in other contexts, do not serve the same purpose as the Affinity Diagram. Heuristic Evaluation focuses on identifying usability issues through established usability principles, rather than analyzing large data sets. A Customer Journey Map is a valuable tool for visualizing the user experience over time, but it is not primarily designed for categorizing data. Stakeholder Interviews provide insights into perspectives and needs from stakeholders but don't involve the systematic categorization and analysis of user data on the scale that the Affinity Diagram does.