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Joint modeling of the association between NIH funding and its three primary outcomes: patents, publications, and citation impact

Author

Listed:
  • Fengqing Zhang

    (Drexel University)

  • Erjia Yan

    (Drexel University)

  • Xin Niu

    (Drexel University)

  • Yongjun Zhu

    (Sungkyunkwan University)

Abstract

This paper examines the impact of NIH funding on research outcomes using data from 108,803 projects funded by NIH between January 2009 and March 2017. We extend the prior knowledge on this topic by incorporating the correlation structure of multiple research outcomes, as well as a comprehensive list of grant-level features capturing information on funding size, gender composition and funding type. Specifically, we utilize partial least squares regression (PLS) to jointly model all three primary outcomes (publications, patents and citation impact) and identify the effects of grant-level features on research outputs. Our results show that joint modeling of research outcomes via PLS yields a more accurate prediction than analyzing each outcome separately. Additionally, we find that when other grant-level features are held constant, a 2-year-longer project duration would produce a similar improvement in research outputs to that achieved by $1 million in additional funding. Based on this finding, we recommend no-cost extension of funded projects instead of increased funding support to achieve a comparable increase in research outputs. Promoting multi-organizational grants is found to be more effective for increasing patents, whereas encouraging multiple-PI grants is more productive in terms of publications and citation impact. Of the various NIH grant types, program project/center grants (P series) and research training grants (T series) are the two most productive and impactful. Results also suggest that projects with a higher proportion of male PIs tend to produce more research outputs. This finding, however, needs to be interpreted with caution due to the limitation of our data set.

Suggested Citation

  • Fengqing Zhang & Erjia Yan & Xin Niu & Yongjun Zhu, 2018. "Joint modeling of the association between NIH funding and its three primary outcomes: patents, publications, and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 591-602, October.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2846-z
    DOI: 10.1007/s11192-018-2846-z
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    3. Shen, Hongquan & Xie, Juan & Ao, Weiyi & Cheng, Ying, 2022. "The continuity and citation impact of scientific collaboration with different gender composition," Journal of Informetrics, Elsevier, vol. 16(1).
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    5. Dzieżyc, Maciej & Kazienko, Przemysław, 2022. "Effectiveness of research grants funded by European Research Council and Polish National Science Centre," Journal of Informetrics, Elsevier, vol. 16(1).
    6. Paul Siu Fai Yip & Yunyu Xiao & Clifford Long Hin Wong & Terry Kit Fong Au, 2020. "Is there gender bias in research grant success in social sciences?: Hong Kong as a case study," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.

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