Data Quality
Detecting and managing poor quality responses - fraud detection, speeders, straightliners, protest responses, and incidence rate monitoring.
Knowledge Base -> Data Quality
SurveyEngine, 09.07.2026
General methodology
Fraud detection in DCE fieldwork - This page collects together the various insights used in fraud detection at SurveyEngine and follows our well received November webinar.
“Incompetents, Accomplices, or Criminals? Panel Fraud in Health Surveys.
Incidence rate monitoring in DCE fieldwork - Incidence rate should be stable throughout fieldwork. A rising IR is the clearest early warning sign of organised panel fraud.
Detecting speeders and straightliners in DCE data - Straightliners and Speeder are a common forms of inattentive responding research. Detecting them early prevents contaminated data from entering your analysis.
Health research
Adverse event monitoring in health preference surveys - In studies conducted in the context of a drug development programme, adverse events disclosed by patients during the survey may trigger regulatory reporting obligations.
Inattentive respondents in health preference DCE studies - Inattentive patient respondents bias WTP estimates in ways that are invisible to standard model fit statistics - and can invalidate regulatory submissions.
Transport research
Diurnal activity checks for transport SP survey fraud detection - When do genuine transport survey respondents complete surveys? Not at 3am. Unusual completion timing is one of the simplest fraud signals available.
RP/SP consistency checks in transport DCE studies - In combined RP/SP transport studies, inconsistencies between revealed and stated preference data reveal respondents who are not engaging genuinely with the stated preference tasks.
Environmental research
Detecting and handling protest responses in environmental DCEs - Protest responses in environmental DCEs are not data quality failures - they are genuine expressions of objection to the study design. But they must be identified and handled correctly.
Scope insensitivity as a data quality indicator in environmental DCEs - Scope insensitivity is not just a validity threat - it is a data quality signal that can identify subgroups of respondents whose choices are not driven by genuine preferences.