Testing for the fairness and predictive validity of research funding decisions: A multilevel multiple imputation for missing data approach using ex‐ante and ex‐post peer evaluation data from the <scp>A</scp>ustrian science fund is a research paper published in Journal of the Association for Information Science and Technology (2014). On theSindex it has a DataRank of 1.2. It has been cited 21 times, with 17 citing works in its 1-hop citation network.
It is essential for research funding organizations to ensure both the validity and fairness of the grant approval procedure. The ex‐ante peer evaluation (EXANTE) of N = 8,496 grant applications submitted to the Austrian Science Fund from 1999 to 2009 was statistically analyzed. For 1,689 funded research projects an ex‐post peer evaluation (EXPOST) was also available; for the rest of the grant applications a multilevel missing data imputation approach was used to consider verification bias for the first time in peer‐review research. Without imputation, the predictive validity of EXANTE was low (r = .26) but underestimated due to verification bias, and with imputation it was r = .49. That is, the decision‐making procedure is capable of selecting the best research proposals for funding. In the EXANTE there were several potential biases (e.g., gender). With respect to the EXPOST there was only one real bias (discipline‐specific and year‐specific differential prediction). The novelty of this contribution is, first, the combining of theoretical concepts of validity and fairness with a missing data imputation approach to correct for verification bias and, second, multilevel modeling to test peer review‐based funding decisions for both validity and fairness in terms of potential and real biases.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.464
From this paper's citation signal
Citation Network Contribution
0.756
From 15 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 38% comes from its base citations and 62% from the citation network (15 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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