6 May
Practical Guide for Increasing Survey Data Quality: Blending Device, Survey, and Historical Signal
We outline a practical framework for improving online survey data quality by integrating behavioral, device, and historical feedback signals. Traditional checks like RLH risk discarding genuine respondents or retaining fraudulent ones, undermining insights. The research demonstrates how combining in-survey behavioral indicators, device fraud detection, and cross-platform feedback loops creates a dynamic, adaptive quality management system. By leveraging Data Quality Co-op's API within Lighthouse Studio, the study highlights holistic, iterative methods for identifying fraud and preserving authentic consumer voices.
