Toward predicting research proposal success

Kevin W. Boyack (), Caleb Smith () and Richard Klavans ()
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Kevin W. Boyack: SciTech Strategies, Inc.
Caleb Smith: University of Michigan Medical School
Richard Klavans: SciTech Strategies, Inc.

Scientometrics, 2018, vol. 114, issue 2, No 8, 449-461

Abstract: 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.

Keywords: Research proposal analytics; Funding success prediction; Discourse analysis; Citation analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11192-017-2609-2

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