N=2,037, in field 20-27 May 2022
The first quantitative research phase established a baseline for some of the key attitudes we would explore further in Phase 3. We piloted some of the question formats which would form the core of the Phase 3 work, including on messages, messengers and views on the various terminology for describing R&D.
Main quantitative data
N=8,474, in field 20-27 July 2022
This nationally-representative sample of over 8,000 people anchors much of our analysis, and allows us to produce granular demographic splits on awareness and attitudes towards R&D. The large sample also supports split-sample questions while maintaining robust sample sizes, allowing us to test different arguments and messengers in isolation. This data set underpins the attitudinal segmentation, explored further in the Segmentation section.
We are also making available the raw data from this survey for those who would like it. Due to the file size, if you fill in the form to the right, we will send you the data in an email.
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Messaging and visual concept quantitative data
N=4,005 (10-20 Feb 2023); N=4,053 (10-19 Feb 2023)
In February 2023, we re-tested several key questions to check for deviation from the 2022 results, and drew in the latest examples of Government R&D narratives since the establishment of a Department for Science, Innovation and Technology in 2023. In addition, these datasets tested written and visual messaging related to R&D in a high level of detail. The 4,005-respondent dataset includes responses to a series of visual concepts (explored in our Visual Concepts section) and the 4,053-respondent dataset includes responses to messages and messaging (described in Terminology and Messages and Messaging).
Terminology quantitative data
N=2,050 (14-7 Oct 2023)
In October 2023, we sought to further understand attitudes towards different terminologies, with a focus on “Research and Development” and “Research and Innovation”. In addition, we tested a set of politically-focused questions ahead of the General Election, as well as exploring attitudes towards the time-lags and negative or positive framings.