We write for Quirk’s about survey fraud and synthetic data solutions

In a recent article for Quirk’s Media, our Head of Research, Steven Snell, PhD, explores the question: “Is Research Ready for Synthetic Data?” 

As synthetic datasets gain momentum, powered by AI and statistical imputation, they promise to extend survey data in new and scalable ways. In his article for Quirks, Steve addresses how the surge in online survey fraud (from click farms to hyperactive bots and more, poses a major threat to the accuracy of our data. Synthetic models trained on compromised responses won’t just replicate errors, they’ll amplify them. 

Drawing on insights from Rep Data’s fraud detection analytics, which identified nearly 30 percent of survey attempts as suspicious in a recent study, Steve emphasizes that synthetic data is only as strong as the quality of its original “seed.” Poor‑quality input yields poor‑quality outputs and potentially greater bias.

So how do we responsibly embrace this powerful tool? Steve outlines a smart path forward in the Quirk’s article:

1. Vet respondents rigorously: Ensure synthetic models learn from reliable, vetted human inputs.
2. Blend synthetic with fresh human data: Use synthetic to supplement real insights, not replace them.
3. Champion transparency: Be clear about training data and model protocols.
4. Monitor and calibrate: Keep synthetic output aligned with real survey results over time.

Synthetic data can be a powerful tool, but its reliability depends entirely on high-quality human input, rigorous respondent validation, and strong fraud mitigation to ensure that imputation models are built on a trustworthy foundation. Steve’s article encourages the industry to proceed thoughtfully, bridging data science innovation with solid quality guardrails.

To learn more, read the full article, “Is Research Ready for Synthetic Data?” on Quirk’s here.