Hiding in Plain Sight: Secondary Analysis of Data Records As a Method for Learning about Citizen Science Projects and Volunteers’ Skills

This paper is the culmination of several facilitated exercises and meetings between external researchers and five citizen science (CS) project teams who analyzed existing data records to understand CS volunteers’ accuracy and skills. CS teams identified a wide range of skill variables that were “hiding in plain sight” in their data records, and that could be explored as part of a secondary analysis, which we define here as analyses based on data already possessed by the project. Each team identified a small number of evaluation questions to explore with their existing data. Analyses focused on accurate data collection and all teams chose to add complementary records that documented volunteers’ project engagement or the data collection context to their analysis. Most analyses were conducted as planned, and included a range of approaches from correlation analyses to general additive models. Importantly, the results from these analyses were then used to inform the design of both existing and new CS projects, and to inform the field more broadly through a range of dissemination strategies. We conclude by sharing ways that others might consider pursuing their own secondary analysis to help fill gaps in our current understanding related to volunteer skills.