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Citizen meets social science: Predicting volunteer involvement in a global freshwater monitoring experiment

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  • Tom A. August
  • Sarah E. West
  • Hannah Robson
  • James Lyon
  • Joseph Huddart
  • Luis F. Velasquez
  • Ian Thornhill


Publication details

JournalFreshwater Science
DateAccepted/In press - 29 Sep 2018
DateE-pub ahead of print (current) - 26 Mar 2019
Issue number2
Number of pages11
Pages (from-to)321-331
Early online date26/03/19
Original languageEnglish


FreshWater Watch is a global citizen science project that seeks to advance the understanding and stewardship of freshwater ecosystems across the globe through analysis of their physical and chemical properties by volunteers. To date, literature concerning citizen science has mainly focused on its potential to generate unprecedented volumes of data. In this paper, we focus instead on the data relating to the volunteer experience and ask key questions about volunteer engagement with the project. For example, we ask what factors influence: a) volunteer data submission following a training event and b) the number of water quality samples volunteers subsequently submit. We used a binomial model to identify the factors that influence the retention of volunteers after training. In addition, we used a generalized linear model (GLM) to examine the factors that affected the number of samples each citizen scientist submitted. In line with other citizen science projects, most people trained did not submit any data, and 1% of participants contributed 47% of the data. We found that the statistically significant factors associated with submission of data after training were: whether training was given on how to upload data, the number of volunteers that attended the training, whether the volunteer was assigned to a research team, the outside temperature, and the average engagement of others in the training group. The statistically significant factors associated with the quantity of data submitted were: the length of time volunteers were active in the project, whether training took place as part of a paid work day, the difficulty of the sampling procedure, how socially involved volunteers were in the project, average sampling group size, and engagement with online learning modules. Based on our results, we suggest that intrinsic motivation may be important for predicting volunteer retention after training and the number of samples collected subsequently. We suggest that, to maximize the contribution of citizen science to our understanding of the world around us, there is an urgent need to better understand the factors that drive volunteer retention and engagement.

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    Research areas

  • Citizen science, Freshwater monitoring, Participation, Training, Volunteer engagement

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