Funding decision-making systems: An empirical comparison of continuous and dichotomous approaches based on psychometric theory is a research paper published in Research Evaluation (2016). On theSindex it has a DataRank of 0.359. It has been cited 5 times, with 3 citing works in its 1-hop citation network.
<p>Psychometrics questions the use of dichotomous decisions. For these reasons, De Los Reyes and Wang (2012) favour a continuous funding decision system, in which the funded percentage of a requested grant sum is coupled to the ratings that a proposal receives in the ex ante peer evaluation. In contrast to the ‘winner takes all’ philosophy in a dichotomous funding decision system, a continuous system takes the low reliability of peer review ratings into account. Funding decisions are mostly based on peer review rating systems that have rather low inter-rater reliability. This article aims to use psychometrics to simulate the two funding decision systems, to compare them to the funding decision system implemented by a real funding organization, and with this, to investigate for the first time the effects of measurement errors on funding decisions. We used peer review data from the Austrian Science Fund (FWF) (N = 8,496 proposals), which obviously implements a hybrid funding decision system. The approval rate at FWF is 44.5%; our findings show that the approval rate would be 32.1% using a purely dichotomous system and 58.4% using a continuous funding decision system. As the funded percentage of a proposal’s requested grant sum increases with increasing mean ex ante peer evaluation of a proposal (<em>r</em> = 0.23), the FWF also shows elements of a continuous funding decision system. Relative to a continuous system, a dichotomous system reduces the approval probability of a proposal overall. This is even the case for high-quality proposals (approval probability ∼0.70).</p>
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.269
From this paper's citation signal
Citation Network Contribution
0.0903
From 3 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 75% comes from its base citations and 25% from the citation network (3 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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