Data Quality
How to keep speeders, duplicates, bots and careless responses out of online survey data.
-
Pre-survey fraud screening: what it catches, what it misses and what it costs
Screening respondents before the survey opens is the cheapest place to remove fraud. It is not free, it is not complete, and it creates false positives you have to plan for.
-
AI agents now pass attention checks. Plan your fieldwork accordingly
A research team built an AI respondent that passed 99.8% of attention checks. That does not mean online sample is finished. It means the checks most studies rely on are aimed at the wrong layer.
-
Ghost completes: the reconciliation problem nobody budgets for
A ghost complete is a complete your sample platform recorded and your survey data does not contain. They are invisible until someone matches IDs, and by then the invoice has usually been approved.
-
The GDQ removal codes, and why free-text reversal reasons have to go
Reconciliation has run on spreadsheets of IDs and reasons like "bad data" for as long as online sample has existed. The Global Data Quality code frame gives buyers and suppliers 18 shared reasons instead. This is how we would use them.
-
A survey data quality checklist for 2026: before, during and after fieldwork
The controls worth having in an online survey this year, sorted by where they run. Short on theory, specific about settings, and honest about which checks have stopped doing their job.
-
Monitor before you enforce: turning on fraud screening without wrecking fieldwork
The fastest way to lose a good supplier is to switch on blocking and watch their traffic disappear without an explanation. Run screening in monitor mode first, join the flags to your own cleaning results, and decide from evidence.