Increasing response rates and reducing bias: Learnings from the Smart Energy Research Lab pilot study

Pre-print - submitted not yet published

Obtaining high-resolution energy consumption data from a large, representative sample of homes is critical for research, but low response rates, sample bias and high recruitment costs form substantial barriers. The wide-spread installation of smart meters offers a novel route to access such data, but in countries like Great Britain (GB) consent is required from each household; a real barrier to large-scale sampling. In this paper we show how certain study design choices can impact the response rate and sample bias in energy studies requesting access to half-hourly smart meter data and (optional) survey completion. We used a randomised control trial (RCT) with a 3 x 2 x 2 factorial design to test incentives, message content/structure and a ‘push-to-web’ approach in a large-scale pilot for the future 10,000+ GB sample. Up to 4 mailings were sent to 18,000 addresses, recruiting 1711 participants (9.5% response rate) from England and Wales. Our results and recommendations can be used to help future energy studies to achieve greater response rates and improved representation. UK-based researchers can apply to use our longitudinal smart meter and contextual datasets.


  • We test recruitment strategies to improve response to a smart energy meter study
  • A conditional monetary incentive increases response and reduces sample bias
  • A push-to-web approach reduces response but significantly increases online sign up
  • Multiple reminders are useful but a 4th mailing is unlikely to be cost-effective
  • Motivational headlines and message structure impact response rates
comments powered by Disqus