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 Austrian science fund

Abstract

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.