Why Quality Matters
High-quality responses:- Provide accurate, thoughtful answers
- Contribute meaningful insights
- Make analysis more reliable
- May contain random or rushed answers
- Can skew your results
- Add noise to your data
Quality Scoring
Deepfield automatically assesses response quality based on several factors.What’s Evaluated
| Factor | What It Measures |
|---|---|
| Completion time | Was the study completed in a reasonable time? |
| Response length | Are open-ended answers sufficiently detailed? |
| Consistency | Do answers make logical sense together? |
| Engagement | Do responses show thoughtful engagement? |
| Audio/Video quality | Is media clear and audible? |
Quality Indicators
Responses may be flagged for:- Speeding: Completed unusually fast
- Straightlining: Same answer for all matrix questions
- Gibberish: Nonsensical open-ended responses
- Poor media: Inaudible or unclear recordings
Viewing Quality Scores
In the Response Table
The response table shows a quality indicator for each response:- High quality: Meets all quality standards
- Medium quality: Some concerns but usable
- Low quality: Significant quality issues
Individual Response Details
Click on any response to see:- Overall quality score
- Specific quality flags
- Details about any issues detected
Quality Factors Explained
Completion Time
Too fast: Participant may have rushed through without reading- Typical flag: Completed in less than 1/3 of average time
- Less concerning than speeding
- May indicate thoughtful responses
Open-Ended Response Length
Too short: Brief answers that don’t provide insight- Example: “good” or “idk”
- Misses the value of qualitative questions
- Complete sentences
- Specific examples or explanations
Straightlining
When participants select the same option for every row in a matrix:- May indicate disengagement
- Could be valid if all items truly rate the same
- Review in context
Consistency
AI checks if answers make logical sense:- Does Q5 answer align with Q3?
- Are there contradictions?
- Do responses tell a coherent story?
Media Quality
For video and audio responses:- Is audio clear and understandable?
- Is video properly recorded?
- Can the response be transcribed?
Managing Quality Issues
Review Low-Quality Responses
Before including in analysis:- Filter to show low-quality responses
- Review each one individually
- Decide whether to include or exclude
Exclude Problematic Responses
Options for handling poor quality:- Exclude from analysis: Don’t include in reports
- Flag for manual review: Review before deciding
- Include with caution: Use but note the limitation
Replace Low-Quality Responses
If quality issues are significant:- Consider recruiting additional participants
- Replace unusable responses with new ones
- Update your quality criteria for future studies
Improving Response Quality
At the Study Design Stage
At the Recruitment Stage
At the Collection Stage
Quality in Analysis
Filtering for Analysis
When generating reports, you can:- Include only high-quality responses
- Set quality thresholds
- Exclude flagged responses
Reporting on Quality
Your analysis may note:- Total responses collected
- Responses meeting quality standards
- Any exclusions and reasons
Common Quality Questions
What's a typical quality pass rate?
What's a typical quality pass rate?
Generally 80-95% of responses meet quality standards. Below 70% may indicate study issues.
Should I exclude all low-quality responses?
Should I exclude all low-quality responses?
Review them first. Some may still contain valuable insights. Others should definitely be excluded.
Why is quality low across the board?
Why is quality low across the board?
Possible causes: Study too long, questions confusing, wrong audience, poor incentive alignment.
Can I improve quality after launching?
Can I improve quality after launching?
Limited options once launched. You can recruit more participants or adjust criteria for new responses.
Quality Checklist
Before analysis, verify:- Reviewed overall quality distribution
- Checked low-quality responses individually
- Decided on inclusion/exclusion criteria
- Documented any quality-related decisions
- Have sufficient high-quality responses for analysis