Research Quality Office Hours

Something feels off? Get a second opinion.

Bring an anonymized study, a concern, or a data quality question to our research team. We will review it with you and give you an independent read before the decision moves forward.

Bring us the case

Something feels off

A study came back and the numbers do not sit right. Your instincts say look closer.

Findings you are not sure you can trust

The results are about to drive a real decision, and you want confidence in the data first.

A supplier or source question

You want an outside read on whether a panel or source is delivering the quality it claims.

Just a question

No dataset yet, only a data quality question you want an experienced researcher to think through with you.

Your study stays anonymized and confidential

An independent read before the decision moves forward

Get a second opinion

Independent Validation

The signals behind the second opinion

We look at the evidence behind the result, not just a headline fraud rate. The review brings together the same signals we use on live fieldwork, from duplicate devices and AI-written open-ends to speeding, straightlining, and contradictions across a survey.

Then a researcher reads those signals in context with you. The goal is a clear, independent explanation you can use.

CONFIDENCE SHIFTINDEPENDENT READOne finding. Two levels of certainty.Independent scrutiny separates signal from unresolved noise.INITIAL READwide rangemore to learnAFTER VALIDATIONtighter rangedecision-readySCRUTINY

A clearer read before the decision moves forward

Free Webinars

Research on Research

Our research team puts the industry's assumptions about fraud and data quality to the test, then presents the findings.

  • Original studies built on billions of survey attempts
  • Fraud, AI, and data quality findings you can cite
  • Each free recording has its own quick form
Watch the webinars

What You Leave With

A clear read, with the reasons spelled out

Not a verdict, an explanation. Every response we review is classified as valid, inattentive, or fraudulent, with the specific reasons behind each flag. And if your data holds up, you will know that too. Independent validation cuts both ways.

01

Bring the case

An anonymized dataset, a topline, or simply the question. No prep deck required.

02

We review it

Your data runs through the same fraud and inattention signals we use on live fieldwork, from duplicate devices and AI-written open-ends to speeding and straightlining, read by a human researcher.

03

Walk through it together

You leave with an independent read and clear next steps, whatever you decide to do.

Reviewed responses
R-1042
Consistent answers, Human response cadence
Valid
R-1043
Duplicate device, VPN detected
Fraud
R-1044
Speeding, Straightlined grid
Inattentive
R-1045
AI-generated open-end
Fraud
R-1046
Failed attention check
Inattentive
R-1047
Coherent open-ends
Valid
32%
average fraud in a typical sample
84%
of it invisible to standard cleaning

Not sure whether to trust the findings?

That question is exactly what office hours are for. Fill out the form above and we will match you with the right researcher.