Working with data
Explore datasets
Use the Dataset Explorer to verify variables, fix labels, and confirm group structure before you tabulate. Three views — Variables, Question Groups, Raw Data — share the same source of truth.
Variables view #
A table of every variable in the dataset showing:
- Variable name (original from the file)
- Label (human-readable description)
- Type badge — colour-coded: C (Categorical), B (Binary), S (Scale/Numeric), T (Text), W (Weight)
- Group membership
Question Groups view #
Variables grouped by their parent question. Useful for:
- Understanding array structures (e.g. a grid question with multiple items)
- Verifying that related variables are grouped correctly
- Identifying groups that need manual adjustment
Raw Data view #
A paginated record-level grid for spot-checking actual values.
- Filter columns to focus on specific variables
- Click a variable in the Variables tab to scroll to it in Raw Data
- Use this to verify suspicious categories, check missing-value patterns, or confirm that a weight variable looks reasonable
Editing metadata #
Variable names, labels, and value labels are editable directly in the explorer. Changes propagate to:
- Table headers and row stubs
- AI agent context (the agent sees updated labels)
- Exported project files
Group-synced editing #
For categorical array questions (grids), editing a value label in one member variable updates it across all members of the group. This prevents inconsistency in array-structured data.