Writing impact case studies: a comparative study of high-scoring and low-scoring case studies from REF2014

This paper reports on two studies that used qualitative thematic and quantitative linguistic analysis, respectively, to assess the content and language of the largest ever sample of graded research impact case studies, from the UK Research Excellence Framework 2014 (REF). The paper provides the first empirical evidence across disciplinary main panels of statistically significant linguistic differences between high- versus low-scoring case studies, suggesting that implicit rules linked to written style may have contributed to scores alongside the published criteria on the significance, reach and attribution of impact. High-scoring case studies were more likely to provide specific and high-magnitude articulations of significance and reach than low-scoring cases. High-scoring case studies contained attributional phrases which were more likely to attribute research and/or pathways to impact, and they were written more coherently (containing more explicit causal connections between ideas and more logical connectives) than low-scoring cases. High-scoring case studies appear to have conformed to a distinctive new genre of writing, which was clear and direct, and often simplified in its representation of causality between research and impact, and less likely to contain expressions of uncertainty than typically associated with academic writing. High-scoring case studies in two Main Panels were significantly easier to read than low-scoring cases on the Flesch Reading Ease measure, although both high-scoring and low-scoring cases tended to be of “graduate” reading difficulty. The findings of our work enable impact case study authors to better understand the genre and make content and language choices that communicate their impact as effectively as possible. While directly relevant to the assessment of impact in the UK’s Research Excellence Framework, the work also provides insights of relevance to institutions internationally who are designing evaluation frameworks for research impact