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Research quality and diversity of funding: A model for relating research money to output of research

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  • Ulf Sandström

    (Linköping University)

Abstract

We analyze the relation between funding and output using bibliometric methods with field normalized data. Our approach is to connect individual researcher data on funding from Swedish university databases to data on incoming grants using the specific personal ID-number. Data on funding include the person responsible for the grant. All types of research income are considered in the analysis yielding a project database with a high level of precision. Results show that productivity can be explained by background variables, but that quality of research is more or less un-related to background variables.

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  • Ulf Sandström, 2009. "Research quality and diversity of funding: A model for relating research money to output of research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(2), pages 341-349, May.
    • Handle: RePEc:spr:scient:v:79:y:2009:i:2:d:10.1007_s11192-009-0422-2
    DOI: 10.1007/s11192-009-0422-2
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    References listed on IDEAS

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    1. Anthony F.J. van Raan, 2006. "Statistical properties of bibliometric indicators: Research group indicator distributions and correlations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 408-430, February.
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    Cited by:

    1. Xianwen Wang & Di Liu & Kun Ding & Xinran Wang, 2012. "Science funding and research output: a study on 10 countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 591-599, May.
    2. Liv Langfeldt & Carter Walter Bloch & Gunnar Sivertsen, 2015. "Options and limitations in measuring the impact of research grants—evidence from Denmark and Norway," Research Evaluation, Oxford University Press, vol. 24(3), pages 256-270.
    3. Belén Álvarez-Bornstein & Adrián A. Díaz-Faes & María Bordons, 2019. "What characterises funded biomedical research? Evidence from a basic and a clinical domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 805-825, May.
    4. Jiang Wu & Miao Jin & Xiu-Hao Ding, 2015. "Diversity of individual research disciplines in scientific funding," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 669-686, May.
    5. Jue Wang & Philip Shapira, 2011. "Funding acknowledgement analysis: an enhanced tool to investigate research sponsorship impacts: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 563-586, June.
    6. Gill, Chelsea & Mehrotra, Vishal & Moses, Olayinka & Bui, Binh, 2023. "The impact of the pitching research framework on AFAANZ grant applications," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    7. ONISHI Koichiro & OWAN Hideo, 2020. "Heterogenous Impacts of National Research Grants on Academic Productivity," Discussion papers 20052, Research Institute of Economy, Trade and Industry (RIETI).
    8. Ashkan Ebadi & Andrea Schiffauerova, 2016. "How to boost scientific production? A statistical analysis of research funding and other influencing factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1093-1116, March.
    9. John Rigby, 2013. "Looking for the impact of peer review: does count of funding acknowledgements really predict research impact?," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 57-73, January.

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