At Loops, we understand that research decisions need to be built on trustworthy data. That's why every response in our platform goes through multiple quality checks before it reaches your dashboard. Here's how we address the most common concerns about online research quality.
Common Quality Concerns
"Are these real people?"
✓ API-connected to vetted panels
We work exclusively with verified panel partners who conduct rigorous identity verification and fraud prevention. Every respondent is authenticated at source.
✓ Zero synthetic data
100% human respondents, always. We never use AI-generated or synthetic responses in your data.
✓ Panel partners named on request
Full auditability of sourcing. We can provide transparency on exactly where your respondents come from.
"Are they paying attention?"
✓ Behavioural monitoring
Our platform flags speed, repetition, and inconsistency in real time, catching inattentive respondents before they complete your study.
✓ Task design deters bots
Unlike simple surveys, concept evaluation and trade-off exercises require genuine comprehension. Our point-and-click methodology means low-effort respondents, bots, and AI-assisted answers are structurally unable to complete tasks—not just filtered out after the fact.
✓ AI-generated response detection
NLP screening identifies LLM-written text in open-ended responses, ensuring you get authentic human feedback.
"Can I trust the output?"
✓ You only pay for quality
Flagged responses are excluded from your study and not charged. You pay only for data that passes our quality standards.
✓ Full traceability
Quality checks are visible in-platform for every study. You can see exactly what filters were applied and review flagged responses.
✓ Market-level quality tracking
We continuously monitor and review panel sources. Declining quality sources are removed from our network to maintain standards across all markets.
The Loops Quality Filter
Every response passes through 7 layers of quality control:
1. Panel Vetting
Identity verified by panel partner before they even reach Loops.
2. Fraud Prevention
Duplicate and bot screening at source catches fraudulent respondents upfront.
3. Task Design
Requires genuine comprehension—low-effort respondents and bots cannot complete our methodology.
4. Speed Detection
Flags unnatural completion times that indicate rushing or inattention.
5. Consistency Checks
Cross-references answer patterns to identify contradictory or random responses.
6. AI Response Detection
NLP screening identifies LLM-written text in open-ended questions.
7. Market Monitoring
Quality tracked per market, with declining sources reviewed and removed.
Why This Matters
Unlike traditional panels that rely on self-reported demographics and simple attention checks, Loops' point-and-click methodology requires active interpretation and evaluation at every step.
This means low-effort respondents, bots, and AI-assisted answers are structurally unable to complete tasks—not just filtered out after the fact. Combined with multi-layer screening, this gives you data you can stake decisions on.
Real people. Real attention. Real confidence.
Have questions about quality measures for a specific study or market? Contact your account manager or reach out to support@loops.com.