Tidy Data
A data organization principle where every column is a variable, every row is an observation, and every cell is a single value. The foundation for efficient analysis and automation.
Definition: A data organization principle where every column is a variable, every row is an observation, and every cell is a single value. The foundation for efficient analysis and automation.
Tidy Data is a set of principles for organizing data in a standardized structure that makes analysis efficient and reproducible.
The Three Rules
- Every column is a variable: Each column represents one specific thing you measured (like "Task Success" or "Time on Task")
- Every row is an observation: Each row represents one unit of analysis (typically one participant)
- Every cell is a single value: Each cell contains exactly one data point
Why It Matters
When data is tidy:
- Analysis becomes systematic and reproducible
- Aggregation (calculating success rates, averages) is straightforward
- Tools and scripts work consistently
- Automation becomes possible
When data is messy—multiple values in cells, variables spread across columns, observations scattered—every analysis requires custom cleanup.
Practical Application
Whether you are analyzing a UX test with 10 participants or a survey with 1,000 respondents, organizing data in tidy format from the start saves enormous time.
If a research platform makes it impossible to export data in tidy format, it may not be the right tool for rigorous analysis.
The tidy data principles, formalized by Hadley Wickham, have become a foundation for modern data analysis workflows.
Related Terms
Quantitative Research
Research focused on numerical measurement with the goal of generalizing findings from a sample to a broader population. Answers 'how much,' 'how many,' and 'how often.'
Research Operations
The orchestration and optimization of people, processes, and craft to amplify the value and impact of research at scale. Often abbreviated as ResearchOps.
Mentions in the Knowledge Hub
This term is referenced in the following articles:
Evaluating AI Research Tools: A Durable Framework
The AI landscape changes weekly. Rather than chasing specific tools, you need a durable framework for evaluating any platform against principles that will not change: privacy, transparency, portability, and reproducibility.
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.
AI-Moderated Interviews: The 'Rag Rug' Data Problem
Tools promising 'adaptive AI interviews' often deliver a data nightmare. Why inconsistent probing creates a patchwork of anecdotes instead of a dataset.
The Art of Moderation: Running Effective Research Sessions
Effective moderation is the invisible craft that separates good research from great research. It requires genuine curiosity, disciplined neutrality, and the ability to create space for authentic participant responses.