Data quality

Data quality begins long before the first participant enters a study.
Every project starts with a careful review of the research objectives, methodology and questionnaire design to ensure that the chosen approach is capable of answering the business question accurately and efficiently.

Where appropriate, we review questionnaire logic, response scales, routing, interview length, respondent burden and measurement validity before fieldwork begins. Pilot testing may also be conducted to identify potential issues before launch. Because even the cleanest dataset cannot compensate for poor research design.

For quantitative studies, we apply a multi-layer quality validation process designed to identify fraudulent, careless or low-engagement responses before analysis begins.

Depending on the project, our quality controls may include:

• respondent identity verification and duplicate detection;
• device fingerprinting and digital fraud screening where available;
• geographic consistency checks;
• completion time analysis to identify speeding;
• straightlining detection;
• attention checks and trap questions;
• logical consistency checks across related questions;
• response pattern analysis;
• manual and AI-assisted review of open-ended responses to identify low-effort, irrelevant, incoherent or copy-pasted content.

Participants who do not meet predefined quality thresholds may be excluded from the final dataset before analysis is conducted.

Quality decisions are based on multiple indicators rather than any single metric, allowing us to distinguish genuine respondent behaviour from isolated anomalies.

For qualitative research, quality begins with recruitment.

Participants are carefully screened against the target profile, and eligibility is verified before each interview or discussion. Throughout the session, moderators monitor engagement, consistency and relevance to ensure that discussions generate meaningful insights rather than superficial opinions.

Analysis follows a structured and transparent process based on systematic coding, thematic interpretation and critical review.

Where appropriate, qualitative findings may also be triangulated with quantitative or behavioural evidence to strengthen confidence in the conclusions.

Behavioural research introduces technical quality requirements that extend beyond traditional survey validation.

Before any behavioural data is collected, participants complete a calibration process specific to the technology being used.

For remote eye tracking, calibration verifies gaze accuracy, tracking stability and recording quality. For facial expression analysis, we assess webcam quality, lighting conditions, participant positioning and facial visibility throughout the recording.

Participants whose recordings do not meet predefined technical quality thresholds are excluded from analysis.

This often results in smaller datasets than the initial recruitment numbers suggest. That is intentional. We report only data that meets our quality standards, not simply everything that was collected.

High-quality research depends on high-quality partners.

We carefully evaluate panel providers, recruitment partners and technology platforms before working with them, considering their participant recruitment practices, fraud prevention procedures, quality assurance processes, data protection standards and alignment with internationally recognised research guidelines.

Our evaluation process draws on industry best practices, including the ESOMAR 37 Questions to Help Buyers of Online Samples, to help ensure that our suppliers meet the standards we expect for every project.

Where appropriate, we are transparent about the partners involved in a project and the role they play within the research process.

Quality assurance does not stop once fieldwork begins.

Throughout data collection, we monitor study performance to identify potential issues as early as possible.

Depending on the methodology, this may include response quality trends, quota fulfilment, dropout rates, interview duration, calibration success rates, recruitment balance and unusual response patterns.

Continuous monitoring enables corrective action before data quality is compromised.

Over the years, we have reviewed data from more than 100,000 research participants across a wide range of studies and methodologies. That experience has reinforced one simple belief: neither technology nor human expertise is enough on its own.

Automated quality controls help us identify patterns, inconsistencies and potential fraud at a scale that would not be possible through manual review alone. Experienced researchers provide what automation cannot: context, methodological judgement and the ability to distinguish genuine behaviour from misleading signals.

We believe the most reliable research comes from combining human expertise with the capabilities of modern technology, not replacing one with the other.

We believe that research quality should never be a black box.
Internally, we distinguish clearly between raw data, cleaned data and analysed data. Every exclusion, validation rule and cleaning decision is documented throughout the research process.

Where appropriate, clients receive documentation describing the quality procedures applied to their project, including exclusion criteria, validation rules and any methodological decisions that may influence the interpretation of results.

Clean data alone does not produce reliable insights.

High-quality evidence requires rigorous methodology, carefully selected participants, transparent quality controls and thoughtful interpretation.

At Signal & Noise, data quality is not simply about removing poor responses. It is about creating a foundation that allows every recommendation, conclusion and business decision to be supported by evidence our clients can trust.

If the data does not meet our quality standards, we would rather question the findings than overstate our confidence in them.