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Toward predicting research proposal success

Author

Listed:
  • Kevin W. Boyack

    (SciTech Strategies, Inc.)

  • Caleb Smith

    (University of Michigan Medical School)

  • Richard Klavans

    (SciTech Strategies, Inc.)

Abstract

Citation analysis and discourse analysis of 369 R01 NIH proposals are used to discover possible predictors of proposal success. We focused on two issues: the Matthew effect in science—Merton’s claim that eminent scientists have an inherent advantage in the competition for funds—and quality of writing or clarity. Our results suggest that a clearly articulated proposal is more likely to be funded than a proposal with lower quality of discourse. We also find that proposal success is correlated with a high level of topical overlap between the proposal references and the applicant’s prior publications. Implications associated with the analysis of proposal data are discussed.

Suggested Citation

  • Kevin W. Boyack & Caleb Smith & Richard Klavans, 2018. "Toward predicting research proposal success," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 449-461, February.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:2:d:10.1007_s11192-017-2609-2
    DOI: 10.1007/s11192-017-2609-2
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Győrffy, Balázs & Herman, Péter & Szabó, István, 2020. "Research funding: past performance is a stronger predictor of future scientific output than reviewer scores," Journal of Informetrics, Elsevier, vol. 14(3).
    3. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    4. Li, Kai & Yan, Erjia, 2019. "Are NIH-funded publications fulfilling the proposed research? An examination of concept-matchedness between NIH research grants and their supported publications," Journal of Informetrics, Elsevier, vol. 13(1), pages 226-237.
    5. Benjamin M. Knisely & Holly H. Pavliscsak, 2023. "Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3197-3224, May.
    6. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    7. Seeber, Marco & Alon, Ilan & Pina, David G. & Piro, Fredrik Niclas & Seeber, Michele, 2022. "Predictors of applying for and winning an ERC Proof-of-Concept grant: An automated machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 184(C).

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