Reduce, Reuse and Recycle: Maximizing the Value of Data in Educational Research 

I have lost count of the number of times I heard the term ‘Zoom fatigue’ last year, but despite its prevalence, we tend not to discuss ‘survey fatigue’. Students in higher education have long been prone to survey fatigue as they are a sought-after group of research participants (Maineri & Can Mol, 2021). Van Mol (2017) reported that in 2014, on average students at a University in Belgium received a survey invitation every 1.5 days. While not quite that frequent, I received countless emails requesting my participation in a survey last year. It certainly felt as though these requests intensified in their frequency, and I reached the point of survey fatigue myself.  

Jowett (2020) explains that COVID-19 is impacting the way we conduct research and consequently researchers are having to redesign their research projects (e.g., by moving data collection online) or suspend them altogether due to lockdowns and social-distancing requirements. This has impacted not only established researchers, but also research students with some having no option but to suspend their studies as they cannot recruit participants or collect data. 

This deluge of survey invitations and the issues faced by fellow researchers (see this post for example) prompted me to contemplate whether the collection of all this data (and other types of data) is necessary. In a recent conversation, my colleague Dwayne Ripley suggested that the simple pneumonic slogan used for environmental awareness could also be applied to data practices to address this issue. He suggested that we should consider how to Reduce, Reuse and Recycle data in order to save both time and energy (of researchers and research participants) and reduce the amount of data that we collect through being more conscious of our own data practices. 

Considering the 3 R’s in education research

We used this analogy as the starting point of our recent AARE conference presentation and prompted education researchers to consider the 3 R’s (Reduce, Reuse, and Recycle) to reduce survey fatigue (and other research fatigue) for participants in education research. It also prompted the idea to reduce some of the time and stress around recruitment and data collection for researchers and research students and to improve both the quality and quantity of data made available to researchers. 

Photo by Sarah Chai on Pexels.com

Here are some simple guidelines to consider: 

Reduce  

Reduce involves taking actions to minimise the amount of data you collect and the subsequent ask on your research participants 

Reuse  

Reuse involves using data more than once 

  • Analyse the data you have using multiple methods and/or theoretical lenses (theoretical or methodological triangulation) 
  • Work off existing data sets, or add to them rather than collecting a whole new one (such as the turn to Big Qual)  

Recycle  

Recycle involves putting data to new use 

  • Consider collecting data that will have long-term use 
  • Add transparency and justification for your data cleaning process so it can more easily be reused by other researchers 

Benefits of the 3 R’s approach 

For education researchers (including research students) there are great benefits to the 3 R’s approach. Utilizing existing data sets allows researchers to begin analyzing data (as soon as they are added to the appropriate ethics application) much sooner and alleviates the need to recruit participants and collect data. This can be invaluable, especially in times of uncertainty around access to research participants such as we have experienced with COVID restrictions. Furthermore, analyzing the same data sets in multiple ways maximizes the value of that data and reduces the number of requests researchers make to students or educators to participate in research. An example by Thompson et al. (2015) of researchers analyzing the same corpus of data using multiple perspectives is available here.  

By using the simple 3 R’s slogan to guide approaches to data collection and analysis, we can shift from limited conceptions of data sets as single-use and individually owned, to conceptions of data sets as collaborative resources with the potential for multiple uses. This collaborative conception of data aligns with the broader open data movement which is calling for many types of data to be made available, including scientific research data being made open to researchers. 

Issues to be considered

Reducing, reusing and recycling data must always be done ethically and where required, seek and obtain human research ethics approval. Maximizing the value of data sets may also require more consideration at the research design and ethics application phases to ensure the data can be reused or recycled in the future.  

Has survey fatigue impacted you? How has COVID-19 impacted your research? 

We encourage you to post your experiences below. 

Banner photo by Emily Morter on Unsplash

About the author

Natasha is a Postdoctoral Fellow at Queensland University of Technology and was formerly a Research Associate in the Business school working on the Connected Learning at Scale project. Her research focuses on the co-construction of environments that support complex problem solving (epistemic environments), online learning, learning analytics, and transitions in education.

Published by Dr Natasha Arthars

Natasha is a Postdoctoral Fellow at Queensland University of Technology and was formerly a Research Associate in the Business school working on the Connected Learning at Scale project. Her research focuses on the co-construction of environments that support complex problem solving (epistemic environments), online learning, learning analytics, and transitions in education.

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