Citizen science is becoming more valuable as a potential source of environmental data. Involving citizens in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. However, a common concern among domain experts is the presumed lower quality of data submitted by volunteers. In this paper, we explore data quality assurance practices in River Watch, a community-based monitoring program in the Red River basin. We investigate how the participants in River Watch understand and prioritize data quality concerns. We found that data quality in River Watch is primarily maintained through universal adherence to standard operating procedures, but there remain areas where technological intervention may help. We also found that rigorous data quality assurance practices appear to enhance rather than hinder the educational goals of the program. We draw implications for the design of quality assurance mechanisms for River Watch and other citizen science projects.