Online crowdsourcing platforms have revolutionized behavioral research by enabling fast, affordable, and diverse data collection. However, ensuring data quality remains a major challenge, especially when participants may be distracted or inattentive. In this blog, we explore how attention checks—particularly task-tailored ones—can effectively filter out low-quality responses in decision-from-experience studies. We also share findings from a recent replication using Connect, which demonstrated significantly higher participant attentiveness and seamless study management. The results affirm that with the right tools and checks, online research can match the reliability of traditional lab experiments.
For years, researchers have used attention check questions to catch people who aren’t paying attention during surveys. These questions often include instructions like, “Select ‘Strongly Agree’ to show you’re paying attention.” While once effective, these checks have become easy to...
This IRB guide explains how Engage ensures ethical research practices, transparent participant recruitment, and data integrity—helping researchers meet institutional review requirements with confidence and ease.
Employee satisfaction isn’t just a buzzword; it’s a critical measure of how retailers attract and retain talent in today’s competitive job market. At CloudResearch, we recently undertook a survey among employees of three big box retailers—Walmart, Best Buy, and Costco—to...
FOR IMMEDIATE RELEASE New feature requires survey respondents to complete surveys twice to identify overly consistent responses, signaling possible AI-generated data. NYC – April 1, 2025 – CloudResearch, a leading online participant recruitment and data quality platform, today announced the...
The session "Cultural Blind Spots: The Hidden Factor in Fraud Prevention" at Quirks LA 2025 highlighted the often-overlooked role of cultural nuances in fraud detection. Leib Litman from CloudResearch and Gene Saykin at Toluna explored how standard fraud prevention measures can unintentionally introduce bias and reduce data quality when cultural differences are not considered. Real-world data and case studies demonstrated the trade-offs between detection efficiency and effectiveness.
