By the same authors

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

Research output: Contribution to journalArticlepeer-review

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Citizen meets social science : Predicting volunteer involvement in a global freshwater monitoring experiment. / August, Tom A.; West, Sarah E.; Robson, Hannah; Lyon, James; Huddart, Joseph; Velasquez, Luis F.; Thornhill, Ian.

In: Freshwater Science, Vol. 38, No. 2, 26.03.2019, p. 321-331.

Research output: Contribution to journalArticlepeer-review

Harvard

August, TA, West, SE, Robson, H, Lyon, J, Huddart, J, Velasquez, LF & Thornhill, I 2019, 'Citizen meets social science: Predicting volunteer involvement in a global freshwater monitoring experiment', Freshwater Science, vol. 38, no. 2, pp. 321-331. https://doi.org/10.1086/703416

APA

August, T. A., West, S. E., Robson, H., Lyon, J., Huddart, J., Velasquez, L. F., & Thornhill, I. (2019). Citizen meets social science: Predicting volunteer involvement in a global freshwater monitoring experiment. Freshwater Science, 38(2), 321-331. https://doi.org/10.1086/703416

Vancouver

August TA, West SE, Robson H, Lyon J, Huddart J, Velasquez LF et al. Citizen meets social science: Predicting volunteer involvement in a global freshwater monitoring experiment. Freshwater Science. 2019 Mar 26;38(2):321-331. https://doi.org/10.1086/703416

Author

August, Tom A. ; West, Sarah E. ; Robson, Hannah ; Lyon, James ; Huddart, Joseph ; Velasquez, Luis F. ; Thornhill, Ian. / Citizen meets social science : Predicting volunteer involvement in a global freshwater monitoring experiment. In: Freshwater Science. 2019 ; Vol. 38, No. 2. pp. 321-331.

Bibtex - Download

@article{9919e28b1e6d470497063673b18dea0f,
title = "Citizen meets social science: Predicting volunteer involvement in a global freshwater monitoring experiment",
abstract = "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.",
keywords = "Citizen science, Freshwater monitoring, Participation, Training, Volunteer engagement",
author = "August, {Tom A.} and West, {Sarah E.} and Hannah Robson and James Lyon and Joseph Huddart and Velasquez, {Luis F.} and Ian Thornhill",
note = "This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2019",
month = mar,
day = "26",
doi = "10.1086/703416",
language = "English",
volume = "38",
pages = "321--331",
journal = "Freshwater Science",
issn = "2161-9549",
publisher = "University of Chicago Press",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Citizen meets social science

T2 - Predicting volunteer involvement in a global freshwater monitoring experiment

AU - August, Tom A.

AU - West, Sarah E.

AU - Robson, Hannah

AU - Lyon, James

AU - Huddart, Joseph

AU - Velasquez, Luis F.

AU - Thornhill, Ian

N1 - This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2019/3/26

Y1 - 2019/3/26

N2 - 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.

AB - 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.

KW - Citizen science

KW - Freshwater monitoring

KW - Participation

KW - Training

KW - Volunteer engagement

UR - http://www.scopus.com/inward/record.url?scp=85063522625&partnerID=8YFLogxK

U2 - 10.1086/703416

DO - 10.1086/703416

M3 - Article

AN - SCOPUS:85063522625

VL - 38

SP - 321

EP - 331

JO - Freshwater Science

JF - Freshwater Science

SN - 2161-9549

IS - 2

ER -