In addition to the data collected through annual questionnaires, Understanding Society collects information using methods such as linking to existing digital data about sample members or collecting bio samples and coding information from them. This paper reports on experiments in the 2024 wave of the Innovation Panel. Respondents were asked for consent to retrieve data on gas and electricity consumption from their smart meter, if they had one, and link it to the survey data. They were also asked whether they would be willing to install an in-home sensor, which collected information on temperature, noise level, light, humidity, and pollutants. We experimented with two aspects of these requests. Previous research has shown that respondents are much less likely to consent to data linkage requests when they answer the consent question online than with an interviewer. In part this seems to be due to respondents processing the request more superficially when they answer the survey online. The first experiment therefore varied how the consent question was formatted: whether as running text or in bullet points, based on the assumption that the visual layout will affect how much information respondents who do not spend much time reading the question will retain.  We will report the effects of the question format on understanding of the consent request (measured with knowledge test questions) and on consent rates. The second experiment contributes to our understanding of how respondents decide whether to participate in additional data collection tasks. In the invitation to the in-home sensor study, we varied the duration of the study (as a manipulation of burden) and we promised part of the sample feedback on the measurements from their sensor (as a personal benefit from participating). We will report on the effects of experimental treatments on participation rates and representativeness.

Co-authors

Dr Jonathan Burton; Professor Annette Jäckle; Jim Vine, University of Essex;

Professor Mick Couper, University of Michigan;

Professor Qunshan Zhao, University of Glasgow;

Mr Martin Pullinger; Dr Simon Elam, University College London

Dr Myngu Zhu, Urban Big Data Centre, University of Glasgow