Where it is
Characteristics in the sidebar (/characteristics/).
Settings
- Enable characteristic collection — master switch
- Require characteristic questions — participants must answer rather than skip
- Anonymous Interview — do not collect the participant's first name
Save Changes stores the settings and any questions waiting to be added.
Adding questions
From the built-in library
Common Demographic Questions lists ten ready-made questions with their variable names: age range, gender, education, employment, income, marital status, household size, region, ethnicity and primary language.
Click Add next to one and it joins a pending list. Nothing is stored until you click Save Changes — leaving the page first discards it.
Your own
Add Question opens a dialog:
- Question Text
- Question Type — Multiple Choice or Fill Slot
- Variable Name — the column name in exports and the key you filter on. Keep it short and snake_case.
- Options — for multiple choice (Add Option for more)
- Slot Description — for Fill Slot, describing what should fill it, e.g. "Number of people, e.g. 2"
- Translations — pick a language and click Translate
Where the answers show up
- Interviews — the table gains a filter per characteristic, so you can narrow to
age_range = 35-44 - Transcripts — shown alongside the conversation
- Exports — one column per variable
- Chat with Data — filterable context
With MCP
get_characteristics {"study_id": "…"}
set_characteristics_settings {"study_id": "…", "enabled": true, "anonymous": false}
create_characteristic_question {"study_id": "…", "question": "What is your age range?",
"variable": "age_range", "question_type": "multiple",
"options": ["18-24","25-34","35-44","45-54","55-64","65+"]}
create_characteristic_question {"study_id": "…", "question": "How many people do you cook for?",
"variable": "cooking_for", "question_type": "slot",
"slot_description": "Number of people, e.g. 2"}
update_characteristic_question {"study_id": "…", "question_id": "…", "options": [...]}
reorder_characteristic_questions {"study_id": "…", "question_ids": ["…", "…"]}
delete_characteristic_question {"study_id": "…", "question_id": "…"}
API writes are immediate — there is no pending list.
Best practices
- Only collect what you will analyze. Every question costs completion rate.
- Use bands, not exact values, for age and income — easier to answer and less identifying.
- Offer a way out on sensitive questions ("Prefer not to say").
- Keep variable names stable across studies so you can compare them.
- Use Anonymous Interview when you have no reason to hold names.
Privacy
Characteristic answers are personal data. Collect the minimum, say in your welcome and consent text what you collect and why, and handle exports according to your own retention policy.
Last updated: September 2026