Review of Protection Against Bots and Fraudulent Survey Submissions in Nursing Research

Fraudulent responses are an ever-present problem in survey research. The articles examined did not routinely report strategies for detecting potentially fraudulent responses or protecting data quality. Published online, survey-based studies that include methods for detecting fraudulent responses enhance reader confidence. Investigators are encouraged to develop an a priori data analysis plan that includes multiple means to identify and eliminate, or otherwise, process fraudulent responses. We suggest that investigators transparently detail the use of a standard checklist for online survey research, in addition to the Fraud detection strategies, Recruitment, Incentive, Excluded responses, Data collection (FRIED) checklist we propose in this article.