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

Fengqing Zhang, Erjia Yan, Xin Niu, Yongjun Zhu

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)591-602
Number of pages12
JournalScientometrics
Volume117
Issue number1
DOIs
Publication statusPublished - 2018 Oct 1

Bibliographical note

Funding Information:
For collaborative grants, we merged organizations, PIs, and total project cost if projects were funded by the same grant. NIH grants use a multi-PI model; therefore, a grant can have more than one PI, and such information is included in ExPORTER. NIH does not, however, use the role of co-PI, so there is no such information in ExPORTER (NIH 2011). We ran a name gender classification algorithm to detect PIs’ genders based on their first names. The algorithm, implemented in genderPredictor (https://github.com/sholiday/ genderPredictor), is based on a naïve Bayes classifier that uses US Social Security Administration name data as the input training data. Its precision is at the 0.85 level, based on a sample of 200 names randomly selected from our data set. This is comparable to the precision obtained from the classifiers used in similar studies (e.g., Liu and Ruths 2013).

Funding Information:
Across the main types of funding provided by NIH, program project/center grants (P series) produced much higher productivity and greater impact than other types of NIH funding. They were followed, in roughly decreasing order of impact and productivity, by research training grants (T series), clinical trial cooperative agreements (U01, U34) and research grants (R series). When funding size and gender composition were controlled for in these analyses, P series remained the most impactful and productive grant type, but the order differed after that point: T series followed next, then small business grants (SBIR/ STTR) and career development awards (K series). This suggests that the two most productive and impactful funding types are P and T series. Additionally, expanding funding support for small business and K series grants might yield a greater benefit than increasing funding for grant types other than P and T series (Tables 3, 4, 5).

Publisher Copyright:
© 2018, Akadémiai Kiadó, Budapest, Hungary.

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

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