Tracking Overview
The response dashboard shows you:- How many people have started
- How many have completed
- How many were screened out
- Progress toward your target sample
Key Metrics
Completion Metrics
| Metric | What It Tells You |
|---|---|
| Starts | Total people who began the study |
| Completes | People who finished all questions |
| Completion Rate | Completes ÷ Starts |
| Average Time | How long participants take |
Screening Metrics
| Metric | What It Tells You |
|---|---|
| Screened Out | Failed qualification criteria |
| Screen Rate | Screened Out ÷ Starts |
| Qualification Rate | Pass screening ÷ Starts |
Response Statuses
Completed
Participant answered all questions and reached the end of the study.- Counts toward your sample target
- Included in analysis
- Full data available
In Progress
Participant started but hasn’t finished.- May still complete
- Partial data available
- Don’t count toward target yet
Screened Out
Participant was terminated by a screening rule.- Did not qualify
- Only screening answers available
- Not included in analysis
Abandoned
Participant started but left without completing.- Chose not to finish
- Partial data may be available
- Doesn’t count toward target
Test
Response from test mode.- Your preview tests
- Not real participant data
- Excluded from analysis
Monitoring Progress
Target vs. Actual
Track your progress:- Target: How many completes you need
- Actual: How many you have
- Remaining: Target minus Actual
- Target: 200 completes
- Completed: 127
- Remaining: 73
Progress by Panel
If you have multiple panels:- See progress for each panel separately
- Identify which panels are lagging
- Adjust recruitment efforts accordingly
Time-Based Tracking
Monitor response flow over time:- Responses per day
- Peak response times
- Trends in recruitment
Identifying Issues
Low Completion Rate
If completion rate is low (below 70-80%):- Study may be too long
- Technical issues possible
- Questions may be confusing
- Check specific drop-off points
High Screen-Out Rate
If screen rate is higher than expected:- Screening criteria may be too strict
- Wrong audience being targeted
- Review your incidence assumptions
Slow Recruitment
If responses aren’t coming in:- Check invitation distribution
- Verify study links work
- Review targeting criteria
- Consider additional recruitment channels
Individual Response Tracking
Response Details
Click on any response to see:- Participant ID
- Start and end times
- Time per question
- All answers given
- Quality assessment
Response Timeline
For each response, you can see:- When they started
- How long they took
- When (or if) they completed
Filtering and Sorting
Sort By
Sort the response list by:- Completion date (newest/oldest)
- Status
- Quality score
- Panel
Filter Options
Filter to see specific responses:- By status (completed, screened out, etc.)
- By panel
- By date range
- By quality score
Tracking Best Practices
Response Notifications
Stay Informed
Set up notifications for:- Reaching completion milestones
- Unusual patterns (high screen-out, low completion)
- Target reached
When to Act
Take action when you see:- Completion rate dropping significantly
- Screen rate much higher than expected
- No responses for extended periods
- Quality scores consistently low
Common Questions
What's a good completion rate?
What's a good completion rate?
Generally 70-85% is healthy. Below 60% suggests possible issues with length or clarity.
Why are people not completing?
Why are people not completing?
Common reasons: Study too long, confusing questions, technical issues, lost interest. Check where drop-offs occur.
Should I contact incomplete participants?
Should I contact incomplete participants?
Generally no—participants who abandon likely chose to do so. Focus on new recruitment.
How long should I wait for completes?
How long should I wait for completes?
Most responses come within the first few days of invitation. After a week, additional invitations or channels may be needed.
Tracking and Analysis
Good tracking helps analysis:- Know exactly how many usable responses you have
- Understand the composition of your sample
- Identify any data quality issues before analysis