Participation in Citizen Science for Coastal Water Data Collection: Does Engagement Drive Persistent Contributions

Last registered on September 25, 2019


Trial Information

General Information

Participation in Citizen Science for Coastal Water Data Collection: Does Engagement Drive Persistent Contributions
Initial registration date
September 25, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
September 25, 2019, 3:05 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.


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Primary Investigator

University of Delaware

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Climate change poses significant risks to coastal livelihoods. Increasing high tides and catastrophic storms challenge flood and water management infrastructure. Many residents and visitors to coastal areas are concerned about the threats of flooding and pollution but do not have the means or training to contribute to solutions. Citizen science describes the collection of data by the general public in collaboration with scientists. Involving the public in projects can provide an avenue where both parties benefit. The scientists receive data that otherwise would be extremely costly to collect, and the citizen scientists are able to act on their concern for their local environment.

The Coastal Observer App encourages citizens to become active in monitoring weather and water locally and will help a team of University of Delaware and Delaware Environmental Institute researchers shape policy and environmental response. However, volunteers have a high opportunity costs of time (the loss of potential benefit from doing an alternative) and economic theory only explains a small portion of volunteer behavior. Economic studies have indicated that many volunteer only when requested to do so.

This experiment builds understanding of citizen scientists’ adoption behavior for and persistent use of data collection practices. Specifically, we will study the decisions of citizen scientists in Delaware to adopt and use the SpotterOn Coastal Observer App designed to assist scientists. This research will also use encouragement design to explore how engagement with scientists might lead to continued use of the app. By addressing various aspects of citizen science and volunteering, this project will generate policy relevant insights on how to effectively promote adoption and persistent use of citizen science tools.

This research will use encouragement design to address if behavioral nudges, such as inviting citizen scientists to engage in the scientific and policy development process, encourage persistent use of the Coastal Observer app.
External Link(s)

Registration Citation

Paul, Laura. 2019. "Participation in Citizen Science for Coastal Water Data Collection: Does Engagement Drive Persistent Contributions." AEA RCT Registry. September 25.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Persistence in use of citizen science tools, such as apps
Primary Outcomes (explanation)
Frequency of posting to the Coastal Observer app over time

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This project is done in collaboration with the Delaware Resilience Awareness Program. They developed the Coastal Observer app which will be launched publicly at the University of Delaware's Coast day.

This project will use an encouragement design to promote uptake of the Coastal Observer app and to provide information on persistent use of Citizen Science tools to the researcher. Participants who register at Coast Day will be randomly assigned into a treatment and control group.
Experimental Design Details
Not available
Randomization Method
Randomization is done using Qualtrics randomizer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
250 in treatment, 250 in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
University of Delaware Institutional Review Board
IRB Approval Date
IRB Approval Number