Developing a methodology to assess the impact of research grant funding: a mixed methods approach

Eval Program Plann. 2014 Apr:43:105-17. doi: 10.1016/j.evalprogplan.2013.12.005. Epub 2013 Dec 26.

Abstract

This paper discusses the development of a mixed methods approach to analyse research funding. Research policy has taken on an increasingly prominent role in the broader political scene, where research is seen as a critical factor in maintaining and improving growth, welfare and international competitiveness. This has motivated growing emphasis on the impacts of science funding, and how funding can best be designed to promote socio-economic progress. Meeting these demands for impact assessment involves a number of complex issues that are difficult to fully address in a single study or in the design of a single methodology. However, they point to some general principles that can be explored in methodological design. We draw on a recent evaluation of the impacts of research grant funding, discussing both key issues in developing a methodology for the analysis and subsequent results. The case of research grant funding, involving a complex mix of direct and intermediate effects that contribute to the overall impact of funding on research performance, illustrates the value of a mixed methods approach to provide a more robust and complete analysis of policy impacts. Reflections on the strengths and weaknesses of the methodology are used to examine refinements for future work.

Keywords: Additionality; Grants; Impact analysis; Mixed methods; Research policy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bibliometrics
  • Humans
  • Interviews as Topic
  • Politics
  • Program Evaluation / economics*
  • Program Evaluation / methods
  • Program Evaluation / statistics & numerical data
  • Public Policy / economics*
  • Public Policy / trends
  • Qualitative Research
  • Research Support as Topic / economics*
  • Research Support as Topic / standards
  • Research Support as Topic / statistics & numerical data