Strategies for the Identification and Prevention of Survey Fraud: Data Analysis of a Web-Based Survey

To assess the impact of COVID-19 on cancer survivors, we fielded a survey promoted via email and social media in winter 2020. Examination of the data showed suspicious patterns that warranted serious review.

The aim of this paper is to review the methods used to identify and prevent fraudulent survey responses.

As precautions, we included a Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), a hidden question, and instructions for respondents to type a specific word. To identify likely fraudulent data, we defined a priori indicators that warranted elimination or suspicion. If a survey contained two or more suspicious indicators, the survey was eliminated. We examined differences between the retained and eliminated data sets.

Of the total responses (N=1977), nearly three-fourths (n=1408) were dropped and one-fourth (n=569) were retained after data quality checking. Comparisons of the two data sets showed statistically significant differences across almost all demographic characteristics.

Numerous precautions beyond the inclusion of a CAPTCHA are needed when fielding web-based surveys, particularly if a financial incentive is offered.