Taxonomy
A classification system that organizes concepts into categories. In research, a predefined set of tags or codes used to systematically categorize qualitative data.
Definition: A classification system that organizes concepts into categories. In research, a predefined set of tags or codes used to systematically categorize qualitative data.
A taxonomy is a hierarchical classification system that organizes concepts, objects, or data into categories based on shared characteristics.
In Research Practice
When analyzing qualitative data, a taxonomy is your predefined set of categories or tags. For example, when coding user feedback, your taxonomy might include:
- Usability Issues
- Feature Requests
- Positive Feedback
- Security Concerns
- Performance Complaints
Why Taxonomies Matter
A well-defined taxonomy:
- Ensures consistency: Different coders apply the same categories
- Enables aggregation: You can count how many instances fall into each category
- Supports comparison: Findings can be compared across studies using the same taxonomy
- Prevents drift: Categories are defined upfront rather than invented during analysis
Taxonomy vs. Ontology
A taxonomy defines categories. An ontology defines the relationships between those categories.
- Taxonomy: "Login Button Issue" and "Navigation Problem" are both types of "Usability Issues"
- Ontology: "Login Button Issue" is a type of "Usability Issue" which affects "User Onboarding"
Using Taxonomies with AI
When using LLMs for thematic analysis, providing a strict taxonomy is critical. It prevents the model from inventing its own categories and ensures output aligns with your analysis framework.
Related Terms
Ontology
A formal representation of the relationships between concepts in a domain. Goes beyond taxonomy to define how categories relate to each other.
Qualitative Research
Research focused on understanding the 'what' and 'why' through rich stories, observations, and context. Seeks depth of understanding rather than statistical measurement.
Mentions in the Knowledge Hub
This term is referenced in the following articles:
What AI Can and Cannot Do for UX Research
AI is not going to take your job, but it is absolutely going to change it. Understanding what LLMs actually are, and are not, is the foundation for using them effectively.
Advanced AI Techniques for Research
Beyond basic prompting, there are techniques that dramatically improve AI reliability: structured communication, using notes over transcripts, treating models as a committee of raters, and understanding when RAG or fine-tuning makes sense.
AI-Assisted Thematic Analysis: A Practical Workflow
The biggest mistake teams make with AI is treating it like a magic black box. Here is a complete, reliable workflow for using LLMs as research assistants while maintaining critical human oversight.
Information Architecture Research: Card Sorting and Tree Testing
Before you design a single screen, the structure of your content must make sense to users. Card sorting and tree testing are specialized techniques for designing and validating information architecture.
Qualitative Thematic Analysis: From Codes to Insights
Transform interview transcripts and observation notes into actionable themes through systematic coding. The difference between an opinion and a finding is whether two people agree.