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Online surveys are a popular method for collecting data in the social sciences. Despite its cost-effectiveness, concerns regarding the legitimacy of data from online surveys are increasing. One such concern is fraudulent responses or “spam” by malicious agents intentionally deceiving the survey process to gain monetary incentives or sway research results. The research costs of “spam”—their influence on research conclusions and their threat to scientific integrity—are not well understood. Here we show the differences in financial and research costs of spam using data from an online survey of transportation workers that was cleaned using a stringent battery of spam detection techniques that utilized commercially available features and a custom spam detection algorithm. We found that we would have wasted about 73% of our budget on incentivizing spammers if we had stopped data collection upon reaching the intended sample size. We also found significant differences in research conclusions related to the relationships between key organizational constructs, including affective commitment, job satisfaction, and turnover intention, between subsamples with and without spam. Our results demonstrate that researchers who are unaware of spam or do not adequately clean their data may spend substantially more monetary and human resources, as well as derive misleading conclusions. This study highlights the importance of survey researchers being cognizant of spam responses and employing robust spam detection techniques to ensure the scientific integrity of non-probability online survey research.