Data Quality

Reliable data. Confident decisions.

Quality is built into the research lifecycle through technology-supported validation and human oversight.

Quality Framework

Multiple layers of protection.

Our quality approach helps identify suspicious participation, inconsistent responses and other signals that can affect research integrity.

Fraud Detection

Identify suspicious patterns and protect research integrity.

IP & Geo Validation

Validate geographic and technical signals where appropriate.

Digital Fingerprinting

Reduce duplicate participation with technology-supported checks.

Attention Checks

Monitor engagement and response consistency.

Speed & Consistency

Flag unusual completion behavior and inconsistent responses.

Manual Quality Review

Human oversight complements automated quality controls.

Process

Define and validate the right audience.

Monitor research participation during fieldwork.

Apply automated and manual checks.

Provide cleaner, decision-ready data.