Studies on the impact of blinding in peer review, including studies that either fully remove the identities of applicants or use other methods to mask or change perceived applicant identity, have shown mixed results as to the benefits of blinding with respect to bias against certain populations. With respect to gender, the range in outcomes include studies that show a reduction in bias with blinded reviews (Johnson and Kirk, 2020; Goldin and Rouse, 2000), those that show no effect between blinded or unblinded reviews (Tomkins et al., 2017; Forscher et al., 2019; Marsh et al., 2008; Ross et al., 2006), and those that find unblinded reviews have less bias in their outcomes (Ersoy and Pate, 2022). Studies evaluating outcomes with respect to race similarly show a range of outcomes from reduction in bias with blinded reviews (Nakamura et al., 2021), to no effect between blinded or unblinded reviews (Forscher et al., 2019).

Other studies have examined if blinding can reduce bias with respect to author institutional affiliation, and several studies have shown reduction in bias towards highly prestigious institutions and authors (Tomkins et al., 2017; Ross et al., 2006; Ersoy and Pate, 2022; Nakamura et al., 2021; Sun et al., 2022). Additionally, one study that examined a process in which an initial blinded review was followed with an unblinded review, found that the reviewers were more likely to increase their scores in the second (unblinded) stage if the author was considered to be from an institution with additional resources and have an author with extensive prior experience, as evidenced through the comments collected from the reviewers (Solans-Domènech et al., 2017). Notably, these studies have used different proxies for ‘institutional prestige’, including published ranked lists of institutions from independent entities (Tomkins et al., 2017; Ross et al., 2006), age of the institution itself (Marsh et al., 2008), list of institutions chosen by the study authors (Ersoy and Pate, 2022), publication records (Sun et al., 2022), and size of the institution by student population (Murray et al., 2016).

The mission of the Arnold and Mabel Beckman Foundation (AMBF) is to fund innovative research projects, especially those that open new avenues of research in chemistry and life sciences. In particular, the foundation’s Beckman Young Investigators (BYI) program seeks to satisfy Dr Arnold O Beckman’s directive to ‘support young scientists that do not yet have the clout to receive major federal research grants,’ and we strive to support outstanding young scientists who are moving into new, transformative areas of scientific inquiry. It stands to reason that our mission could be more effectively fulfilled if we ensure that our review process is insulated as much as possible from implicit or explicit gender or institutional prestige bias by reviewers.

In this article we report the results of a multi-year study to assess the impact of blinding gender and institutional affiliation in the first stage of the application process for BYI awards, which involves applicants submitting a technical proposal as part of an initial Letter of Intent (LOI). In 2020 applicants were instructed to blind their technical proposals by omitting their name, gender, gender-identifying pronouns, and institutional information. We have examined the impact of this change in policy by comparing data on all stages of process (from the initial review through to the award of grants) for the four years before the new policy was introduced (2017–2020) and the first four years of the new policy (2021-2024). We were not able to perform a similar analysis with regards to race as our application process during unblinded review years did not collect race or ethnicity information from applicants.

Throughout the years included in this study, 2017–2024, the criteria for applicant eligibility as well as the overall review process were not substantively changed. Thus, in the absence of institutional prestige or gender bias in our review process, the distribution of advancing LOIs and program awards before and after blinding should be similar. This was the case when considering gender distribution, providing no evidence of gender bias in either the unblinded or the blinded reviews. However, upon blinding at the LOI stage, we did find a reduction in the relative advantage of more prestigious institutions in advancing to a full application invitation and in receiving a program award. We therefore conclude that there was an institutional prestige bias in our review process, and that blinding helped to reduce the impact of that bias. This reduction in bias brings us closer to our goal of the equitable allocation of research funding resources based on scientific merit that is not influenced by implicit or explicit institutional prestige bias from reviewers.

The total number of reviewed LOIs, Full Applications invitations, and BYI Program Awards in program years 2017–2020 (unblinded) and 2021–2024 (blinded) is presented in Table 1; the 2024 Program Awards have not been completed as of manuscript preparation.

Institutional prestige

To examine the extent to which institutional prestige bias may have influenced our LOI review process, we developed eight institutional ranking schema, further divided into five institutional categories, based on published rankings of institutions from four independent organizations as well as from an internal analysis of historical AMBF funding trends, described further in the Methods section. We then calculated the Relative Advantage–Full Application across institutional categories for each schema: a ratio of the percentage of LOIs submitted in a particular institutional category that received an invitation to submit a full application to the percentage of all LOIs that received a full application invitation. If there was no implicit or explicit institutional prestige bias from our reviewers in the LOI review process, then we would expect the Relative Advantage–Full Application would be the same in the unblinded and blinded reviews. Therefore, we examined the difference in Relative Advantage–Full Application between the unblinded and blinded reviews to determine if there was institutional prestige bias in our review process. We repeated the analysis to determine the Relative Advantage–Award for each institutional category as a ratio of the percentage of LOIs submitted in a particular institutional category that received a program award to the percentage of all LOIs that received a program award. Again, we examined the difference in Relative Advantage–Award from unblinded and blinded reviews to determine if there was institutional prestige bias in our review process.

Figure 1 shows the average Relative Advantage–Full Application afforded to an LOI applicant to receive an invitation to submit a full application within an institutional category for each of the eight ranked institutional lists used in this study, when comparing the unblinded and blinded reviews. During the unblinded reviews, the range of the Relative Advantage–Full Application for a full application invitation for an LOI from the ‘1–10’ institutional categories was 1.4–1.7 times higher than the average percentage, and the range for the ‘11–25’ institutional categories was 1.2–1.4 times higher, illustrating a consistent bias in favor of more prestigious institutions. After requiring the submission of blinded LOIs, the Relative Advantage–Full Application for an invitation for a full application from both the ‘1–10’ and ‘11–25’ institutional categories decreased to 1.0–1.3 times the average. Importantly, the change in the Relative Advantage–Full Application of receiving a full application invitation for those from the ‘Other’ institutional category increased from 0.60 to 0.83 times the average during unblinded reviews to 0.81–0.93 with the blinded reviews. This increase is significant as the potentially transformative ideas from junior faculty at these institutions, which might otherwise have been lost to the institutional prestige bias in the unblinded review, are now being considered at the full application review stage. Finally, we found that the ranges for the Relative Advantage–Full Application of receiving a full application invitation for applicants in the ‘26–50’ and ‘51–100’ categories remained unchanged between the blinded and unblinded review processes, being 0.83–1.0 times and 0.80–0.95 times lower than the average percentage respectively.

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BYI LOIs, Full Application Invitations, and Program Awards by institutional category.

All LOIs Received, Full Application Invitations, and Program Awards by institutional category from 2017 to 2020 (unblinded) and 2021–2024 (blinded).

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To test for systemic bias, the average value and ranges of the Relative Advantage–Full Applications by institutional category and the results of Chi-squared tests of association between institutional category and full application invitation status within the blinded and unblinded processes for each institutional ranking schema are presented in Table 2. The relationship between institutional category and full application invitation status was statistically significant when reviews were unblinded, across all institutional ranking schema. This association represents a medium sized effect (as measured by Cramer’s V; range = 0.14–0.23, average = 0.20). After changing to the blinded review process, the relationship between institution category and full application invitation status is not consistently statistically significant (depending on the institutional ranking schema), and the effect size of the association is decreased compared to that under the unblinded review process (as measured by Cramer’s V; range 0.08–0.15; average = 0.13). We find that the change to the blinded review process resulted in a consistent decrease in the Chi-squared as well as the Cramer’s V statistic for each institutional ranking list, and we conclude that blinded review reduced the impact of institutional prestige bias on full application invitation rates.

Table 2

Ranked List: NCSES 2018				Ranked List: NCSES 2020
Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded	Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded
1–10	1.6 (1.4–1.9)	1.0 (1.0–1.1)	0.6	1–10	1.5 (1.3–1.7)	1.2 (1.1–1.3)	0.3
11–25	1.2 (1.0–1.3)	1.2 (1.0–1.4)	0	11–25	1.2 (1.1–1.2)	1.1 (0.9–1.3)	0.1
26–50	0.93 (0.70–1.3)	1.0 (0.86–1.1)	–0.07	26–50	0.87 (0.63–1.3)	0.97 (0.86–1.1)	–0.1
51–100	0.90 (0.66–1.1)	0.89 (0.79–0.95)	0.01	51–100	0.97 (0.65–1.2)	0.95 (0.89–1.0)	0.02
Other	0.70 (0.60–0.82)	0.87 (0.69–1.1)	–0.17	Other	0.71 (0.61–0.82)	0.84 (0.65–0.99)	–0.13
Analysis	Unblinded	Blinded	Unblinded - Blinded	Analysis	Unblinded	Blinded	Unblinded - Blinded
Chi-squared	48.64	9.47	39.17	Chi-squared	44.9	10.35	34.55
p (d.f.=4)	6.95E-10	0.0503		p (d.f.=4)	4.17E-09	0.0349
Cramer’s V	0.19	0.1	0.09	Cramer’s V	0.18	0.11	0.07
Effect Size	Medium	Small		Effect Size	Medium	Small
Ranked List: Shanghai Ranking 2018				Ranked List: Shanghai Ranking 2023
Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded	Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded
1–10	1.6 (1.5–1.8)	1.3 (1.0–1.7)	0.3	1–10	1.6 (1.4–1.9)	1.2 (1.1–1.4)	0.4
11–25	1.4 (1.4–1.7)	1.2 (1.0–1.3)	0.2	11–25	1.4 (1.1–1.6)	1.2 (1.1–1.3)	0.2
26–50	0.85 (0.80–0.91)	0.96 (0.82–1.1)	–0.11	26–50	0.88 (0.75–1.0)	1.0 (0.92–1.1)	–0.12
51–100	0.83 (0.64–1.0)	0.82 (0.69–0.91)	0.01	51–100	0.80 (0.67–0.91)	0.80 (0.62–0.86)	0
Other	0.61 (0.38–0.80)	0.83 (0.61–1.1)	–0.22	Other	0.62 (0.43–0.76)	0.84 (0.61–1.2)	–0.22
Analysis	Unblinded	Blinded	Unblinded - Blinded	Analysis	Unblinded	Blinded	Unblinded - Blinded
Chi-squared	71.87	17.84	54.03	Chi-squared	70.85	21.94	48.91
p (d.f.=4)	9.14E-15	0.00133		p (d.f.=4)	1.50E-14	0.000206
Cramer’s V	0.23	0.14	0.09	Cramer’s V	0.23	0.15	0.08
Effect Size	Medium	Small		Effect Size	Medium	Medium
Ranked List: Times Higher 2018				Ranked List: Times Higher 2023
Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded	Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded
1–10	1.5 (1.3–1.6)	1.3 (1.2–1.4)	0.2	1–10	1.6 (1.4–1.6)	1.3 (1.2–1.4)	0.3
11–25	1.4 (1.2–1.7)	1.2 (1.1–1.3)	0.2	11–25	1.4 (1.2–1.6)	1.2 (1.2–1.2)	0.2
26–50	0.86 (0.76–1.1)	0.89 (0.83–0.96)	–0.03	26–50	0.84 (0.63–0.98)	0.91 (0.77–1.1)	–0.07
51–100	0.85 (0.67–1.1)	0.91 (0.73–1.1)	–0.06	51–100	0.82 (0.56–1.1)	0.83 (0.81–0.92)	–0.01
Other	0.66 (0.55–0.76)	0.81 (0.53–1.2)	–0.15	Other	0.69 (0.62–0.75)	0.88 (0.65–1.1)	–0.19
Analysis	Unblinded	Blinded	Unblinded - Blinded	Analysis	Unblinded	Blinded	Unblinded - Blinded
Chi-squared	60.03	19.5	40.53	Chi-squared	60.78	17.64	43.14
p (d.f.=4)	2.86E-12	0.000626		p (d.f.=4)	1.98E-12	0.00145
Cramer’s V	0.21	0.15	0.06	Cramer’s V	0.21	0.14	0.07
Effect Size	Medium	Medium		Effect Size	Medium	Small
Ranked List: Leiden 2018–2021				Ranked List: AMBF 1990–2018
Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded	Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded
1–10	1.4 (1.1–1.8)	1.0 (0.88–1.2)	0.4	1–10	1.7 (1.5–2.0)	1.3 (1.2–1.3)	0.4
11–25	1.2 (1.1–1.3)	1.2 (1.1–1.2)	0	11–25	1.2 (0.83–1.4)	1.2 (1.1–1.2)	0
26–50	0.83 (0.70–0.92)	0.98 (0.89–1.1)	–0.15	26–50	1.1 (1.0–1.2)	0.90 (0.70–1.1)	0.2
51–100	0.95 (0.76–1.1)	0.94 (0.78–1.3)	0.01	51–100	0.86 (0.56–1.0)	0.87 (0.70–1.0)	–0.01
Other	0.83 (0.65–1.0)	0.93 (0.71–1.1)	–0.1	Other	0.60 (0.54–0.72)	0.86 (0.70–1.2)	–0.26
Analysis	Unblinded	Blinded	Unblinded - Blinded	Analysis	Unblinded	Blinded	Unblinded - Blinded
Chi-squared	27.96	5.23	22.73	Chi-squared	74.22	20.34	53.88
p (d.f.=4)	1.27E-05	0.265		p (d.f.=4)	2.91E-15	0.000427
Cramer’s V	0.14	0.08	0.06	Cramer’s V	0.23	0.15	0.08
Effect Size	Small	Small		Effect Size	Medium	Medium

In addition to the individual institutional ranking lists used above, we also created a Consensus Institutional Ranking list by averaging the ranking of the 96 institutions that appeared on at least five of the individual lists. We reasoned that this consensus list might best mirror how our reviewers experience the reading of these individual lists over time and how they consolidate this information into their own heuristic of institutional prestige. We then repeated the calculation of the Relative Advantage–Full Application against the Consensus Institutional Ranking list.

Figure 2 shows the average Relative Advantage–Full Application afforded to an LOI applicant to receive an invitation to submit a full application within a category in the consensus list, when comparing the unblinded and blinded reviews. During the unblinded reviews, the range of the Relative Advantage–Full Application for a full application invitation for an LOI from the ‘1–10’ and ‘11–25’ categories in the consensus list was 1.6 times and 1.3 times higher than the average percentage respectively, confirming a consistent bias in favor of more prestigious institutions. After requiring the submission of blinded LOIs, the Relative Advantage–Full Application for an invitation for a full application from the ‘1–10’ and ‘11–25’ categories both decreased to 1.2 times the average. Importantly, the change in the Relative Advantage–Full Application of receiving a full application invitation for those from the ‘26–51’ and ‘Other’ categories again increased from 0.85 times and 0.70 times the average during unblinded reviews, to 1.0 times and 0.88 times the average with the blinded reviews. Finally, consistent with the findings in the individual lists, we found that the Relative Advantage–Full Application of receiving a full application invitation for applicants in the ‘51–96’ category in the consensus list decreased from the blinded and unblinded review processes, from 0.80 times to 0.73 times lower than the average percentage respectively.

Figure 2

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The average value and ranges of the Relative Advantage–Full Applications by institutional category in the consensus list, and the results of Chi-squared tests of association between institutional category and full application invitation status within the blinded and unblinded processes, are presented in Table 3. As before, the relationship between institutional category and full application invitation status was statistically significant when reviews were unblinded. This association represents a medium sized effect as measured by Cramer’s V=0.22. After changing to the blinded review process, the effect size of the association is decreased compared to that under the unblinded review process with Cramer’s V=0.15. We find that the change to the blinded review process resulted in a consistent decrease in the Chi-squared as well as the Cramer’s V statistic. Thus, we conclude that the analysis with the Consensus Institutional Ranking list also shows that the blinded review reduced the impact of institutional prestige bias on full application invitation rates.

Table 3

Ranked List: Consensus
Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded
1–10	1.6 (1.4–2.0)	1.2 (1.1–1.4)	0.4
11–25	1.3 (1.3–1.4)	1.2 (1.1–1.6)	0.1
26–50	0.85 (0.73–0.93)	1.0 (0.90–1.1)	–0.15
51–100	0.80 (0.67–1.0)	0.73 (0.45–1.0)	0.07
Other	0.70 (0.53–0.80)	0.88 (0.67–1.1)	–0.18
Analysis	Unblinded	Blinded	Unblinded - Blinded
Chi-squared	66.87	21.9	44.97
p (d.f.=4)	1.04E-13	0.00021
Cramer’s V	0.22	0.15	0.07
Effect Size	Medium	Medium

We continued the analysis of our review outcomes relative to institutional prestige through to program awards for the study years to determine if the change in institutional representation present in the submitted full applications would extend also to program awards. We calculated the Relative Advantage–Award across all institutional category schema described above: a ratio of the percentage of LOIs submitted in a particular institutional category that received a program award to the percentage of all LOIs that received a program award. As before, if institutional prestige bias did not influence the awardee selection process, then the Relative Advantage–Award would be the same between unblinded and blinded reviews, and we examined the difference in Relative Advantage–Award to determine if there was institutional prestige bias in our selection process. Due to the limited number of program awards that are selected each year, we would expect an average of two awards per category each year if there is no institutional prestige bias. The analysis of program awards with the Consensus Institutional Ranking list is presented here in Figure 3 and Table 4, and the results of the analysis for all institutional categories is presented in Figure 1—figure supplement 1 and Table S1 in Supplementary file 1. In addition, the blinded review data has only three years of awardees, as the fourth year of awardee selection has not been finalized as of manuscript preparation.

Figure 3

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Table 4

Ranked List: Consensus
Category	Unblinded Average (Range)	Blinded* Average (Range)	Unblinded - Blinded
1–10	2.5 (1.6–3.4)	1.8 (0.63–2.6)	0.7
11–25	2.0 (0.61–3.3)	1.4 (1.1–2.6)	0.6
26–50	0.23 (0.0–0.52)	0.91 (0.70–1.1)	–0.68
51–100	0.54 (0.0–1.0)	0.46 (0.0–0.83)	0.08
Other	0.42 (0.33–0.68)	0.83 (0.42–0.90)	–0.41
Analysis	Unblinded	Blinded*	Unblinded - Blinded
Chi-squared	30.62	5.18	25.44
p (d.f.=4)	3.66E-06	0.269
Cramer’s V	0.15	0.09	0.06
Effect Size	Medium	Small

Figure 3 shows the average Relative Advantage–Award afforded to an LOI applicant to receive program award within the categories in the consensus list, when comparing the unblinded and blinded reviews. During the unblinded reviews, the average of the Relative Advantage–Award for an LOI from the ‘1–10’ and ‘11–25’ categories was 2.5 times and 2.0 times higher than the average percentage respectively. With this relative advantage, on average 75% of AMBF’s annual program awards were to the top 25 institutions ranked on this list, out of the 287 institutions who applied to the BYI Program during the study years. After requiring the submission of blinded LOIs, the Relative Advantage–Award for a program award from the ‘1–10’ and ‘11–25’ categories decreased to 1.8 times and 1.4 times the average respectively, which represents an average of 45% of the annual program awards. While still an advantage for the top ranked institutions, the decrease in awards to these top institutions open opportunities for the highly rated candidates from other institutions to receive a BYI Program award. The change in the Relative Advantage–Award for those from the ‘26–50’ category increased with the blinded reviews from 0.23 to 0.91 times the average percentage, and the change for the ‘Other’ category was from 0.42 to 0.83 times the average. Finally, we found that the Relative Advantage–Award for applicants in the ‘51–96’ category decreased moderately from 0.54 to 0.45 times lower than the average percentage during blinded reviews.

Again, the average value and ranges of the Relative Advantage–Award by category in the consensus list, and the results of Chi-squared tests of association between institutional category and program award status, within the blinded and unblinded processes is presented in Table 4. The full set of average values and ranges for each institutional category schema is presented in Table S1 in Supplementary file 1. The relationship between category in the consensus list and program award status was statistically significant when reviews were unblinded, with a medium sized effect as measured by Cramer’s V=0.15. After changing to the blinded review process, the relationship between category and program award status is not statistically significant, and the effect size of the association is decreased compared to that under the unblinded review process. We find that the change to the blinded review process resulted in a decrease in the Chi-squared as well as the Cramer’s V statistic for the consensus list, and we conclude that blinded review at the LOI stage also contributed to reducing the impact of institutional prestige bias on program award rates.

Gender

In addition to institutional prestige bias, we assessed our review process relative to potential gender bias, with assignment of applicants into female or male gender categories as described in the Methods section. Figure 4 shows the percentage of female applicants that submitted an LOI, received a full application invitation, and received a program award over the years of the study. The percentage of full application invitations to female applicants is consistent with the percentage of LOIs received from female applicants over the years studied, to include unblinded and blinded reviews. The percentage of program awards has a higher variability, but the average of the study years again tracks the percentage of female LOI applicants with no evidence of gender bias in the review process.

Figure 4

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Figure 4—source data 1

BYI Letters of Intent (LOIs), Full Application Invitations, and Program Awards by gender category.

All LOIs Received, Full Application Invitations, and Program Awards by gender category for 2017–2020 (unblinded) and 2021–2024 (blinded).

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Using the same methodology as the analysis of institutional prestige, we calculated the Relative Advantage–Gender as the ratio of the percentage of each gender to receive a full application invitation or program award relative to the percentage of any applicant to receive a full application invitation or program award. Table 5 presents the average value and ranges of the Relative Advantage–Gender for the two gender categories and the results of Chi-squared tests of association between gender category and full application invitation or program award status within the blinded and unblinded processes. The relationship between gender category and full application invitation or program award status is not statistically significant, and there is no effect of gender bias from the review process as seen by the Cramer’s V of 0.00–0.03 across all conditions studied. We therefore conclude that our review process and subsequent program awards did not demonstrate gender bias, either before or after blinding the LOI reviews.

Table 5

Full Application Invitations				Program Awards
Category	Unblinded Average (Range)	Blinded Average (Range)	Unblinded - Blinded	Category	Unblinded Average (Range)	Blinded* Average (Range)	Unblinded - Blinded
Male	1.0 (0.94–1.1)	1.0 (1.0–1.1)	0	Male	0.99 (0.71–1.3)	0.96 (0.77–1.1)	0.03
Female	0.99 (0.84–1.2)	0.93 (0.90–0.98)	0.06	Female	0.96 (0.38–1.4)	1.0 (0.84–1.3)	–0.04
Analysis	Unblinded	Blinded	Unblinded - Blinded	Analysis	Unblinded	Blinded*	Unblinded - Blinded
Chi-squared	0.06	0.88	–0.82	Chi-squared	0.0	0.01	–0.01
p (d.f.=1)	0.799	0.347		p (d.f.=1)	1.0	0.916
Cramer’s V	0.01	0.03	–0.02	Cramer’s V	0.00	0.00	0.00
Effect Size	No effect	No effect		Effect Size	No effect	No effect

Overall, this work illustrates one of the major challenges AMBF and other funders face when supporting young scientists. We seek to find the exceptional individuals conducting transformative science at all institutions, not just those that are at institutions perceived by reviewers to have higher prestige. However, in the drive to support the most exciting and innovative researchers and ideas, it is apparent that the qualitative metric of institutional affiliation (whether explicit or implicit) often used in applicant assessment may not be a good proxy for scientific innovation. The bias we found towards prestigious institutions in the unblinded reviews reveals that faculty at institutions with a lesser reputation are not afforded the same allowances from reviewers as those applicants at institutions with a greater reputation.

We observed that the institutional prestige in our review process was reduced, but not eliminated, by blinding the LOI applications, indicating that there may be other bona fide measurable differences between institutional categories. The origin of this difference may be a combination of the quality of the candidates themselves, the physical research infrastructure of their universities, and the resources and support available to junior faculty at well-resourced institutions, including in submitting applications to funding opportunities. There also may have been additional factors that influenced our results between the application cycles. For example, we did receive fewer overall applications in blinded years of our study due to COVID-19 research disruptions and hiring freezes across institutions. However, LOIs were received in each institutional category consistently across the studied years indicating that our initial populations were composed of LOIs with similar institutional diversity, as shown in Table 6. Additionally, we informed our reviewers at the beginning of the review process that the purpose in blinding the LOIs was in part to study if there was institutional prestige bias in our reviews. This awareness may have impacted the scoring of proposals if the reviewers were more willing to advance LOIs or full applications that had weaknesses attributed to lack of mentorship or access to resources, which might indicate that the applicant was from an institution that did not have the same level of research support infrastructure.

There is no universally accepted ranking of institutions and therefore there are many possible methods that can be used to define and measure the conceptual quality of institutional prestige. Every reviewer brings to the review process their own unique perception of institutional prestige based on the qualities that they value most which may also influence any implicit or explicit bias. For this study, we chose several lists that used different measurable variables in their institutional rankings, including federal research funding received, research publications and citations by faculty, prestigious awards received by faculty, and student outcomes, among other variables. These variables were likely important to our reviewers and are relevant to our mission of supporting basic science, however they may or may not have covered all possible variables or may have been too restrictive. For example, the Leiden ranking list had the lowest correlation with our review outcomes, possibly because the filter settings we used for that list included publications within biomedical and related journals, which may not have accurately reflected our total applicant and reviewer pool that come from disciplines across chemistry, biology, and life sciences. Also, the ranked list based on historical AMBF funding was not provided to any reviewers or previously published but was included because of our own internal reviewer recruitment practices.

Anecdotally, reviewers who participated in the BYI LOI reviews reported that the blinded materials were easier to read, that there was a significant reduction in their workload to review the same number of LOIs, and the review meetings were shorter as it was easier to focus solely on the merits of the technical proposal during the discussion. With these observed benefits in reducing institutional prestige bias and the positive comments from our reviewers, AMBF plans to continue to use the blinded review for the BYI LOI application step, and we have also adopted these same review methods to our Postdoctoral Fellowship program application process. In sharing these results, AMBF hopes this information will be informative for others moving forward with evaluation of their application review processes with the goal of instituting more equitable practices (Franko et al., 2022), especially for those organizations with missions and funding programs similar to our own.

All data generated or analysed during this study are included in the manuscript and associated source data files.

1. Website

   1. AMBF

   (2023) Beckman Young Investigator Blinded LOI Instructions

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Decision letter after peer review:

Thank you for submitting your article "Blinding Reduces Institutional Prestige Bias During Initial Review of Applications for a Young Investigator Award" to eLife for consideration as a Feature Article. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Peter Rodgers of the eLife Features Team. Both reviewers have opted to remain anonymous.

The reviewers and editors have discussed the reviews and we have drafted this decision letter to help you prepare a revised submission. Addressing the comments listed below will require substantial work, so you may prefer to submit the manuscript to a different journal: if you decide to do this, please let me know.

Summary:

In this manuscript the authors report that blinding the identity of applicants during the first round of review for applications for a Beckman Young Investigator Award led to a reduction in prestige bias. This topic is of central importance for fair scientific funding allocation. The manuscript is clearly written, and some of the key limitations are openly discussed. However, there are a number of concerns that need to be addressed, notably concerning the proxy for prestige used by the authors. Moreover, the authors' dataset could also be used to explore possible gender bias in the review for these awards.

Essential revisions

1. The manuscript, as it stands, does not bring much more to the topic than what has been published in previous papers, such as Solans-Domenech et al., 2017, which analyzed a larger sample (and is not cited by the authors – see point 4 below). In order to improve the manuscript I would advise the authors to expand the scope as follows:

1a. The option and justification given by the authors of not studying the effect of gender is not convincing. Although applicants were not asked about their gender before 2021, it could still be possible to perform an analysis based on a proxy where first names would be associated to gender.

1b. The following point is optional: It would be interesting to check the final outcome of the evaluation, at the second step, when the proposal is not blinded anymore, and to assess if the final set of successful candidates (in terms of affiliations) differ from those before the blinding step was introduced.

2. The prestige ranking is based on total funding size, which is certainly correlated with the prestige of the institutions. However, this measure is also highly correlated with the size of the institutions, which might not accurately reflect the prestige: for example, Harvard, Yale and MIT are in the second tier; Chicago, Berkeley and Caltech are in the third tier; and there are also some very prestigious institutions (such as Rockefeller University, Fred Hutchinson Cancer Research Center, and the Mayo Clinic) in the bottom tier.

This point could be addressed by using a modified prestige ranking obtained by normalizing the funding size with respect to the size of the institutions (e.g., the number of researchers) might be more appropriate.

Another approach would be to use other university rankings (eg Shanghai, THE, Leiden, etc).

Our suggestion is that several different proxies for prestige should be used in the study (eg two or more of NSF, NSF normalized, Shanghai, THE, Leiden, and maybe even the proxy based on previous performance when applying to Beckman), and that the results of all the proxies studied should be reported.

3. There are a number of points regarding the statistical analysis that need to be addressed

3a. Regarding the NSF ranking: the 2018 data has been used, but as the study covers 8 years, the authors should at least have tested if rankings from different years would give different results.

3b. The division of prestige categories into four groups based on equal partition of total national funding, while reasonable, still seems a bit arbitrary. It might be of interest to test the robustness of the results with respect to the number of categories chosen.

3c. The sample size in the treatment group (data from 2021-2023) is substantially smaller than the sample size in the control group (data from 2016-2020). Therefore, the fact that the p-value becomes insignificant might simply be because of reduced sample size instead of reduced prestige bias. Down-sampling the institutions for 2016-2020 might make the comparison more fair. Or alternatively, a difference-in-difference design might be able to show the effect more clearly.

4. The discussion of the existing literature on bias in research grant evaluation needs to be improved. I recommend that the authors cite and discuss the following articles:

Forscher, P.S., Cox, W.T.L., Brauer, M. et al. (2019) Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. Nat Hum Behav 3:257-264.

Marsh et al. (2008) Improving the peer-review process for grant applications: Reliability, validity, bias, and generalizability. American Psychologist, 63:160-168.

Solans-Domenech et al. (2017) Blinding applicants in a first-stage peer-review process of biomedical research grants: An observational study. Res. Eval. 26:181-189. DOI:10.1093/reseval/rvx021

I also recommend that the authors cite and discuss the following article about prestige bias in the peer review of papers submitted to a conference:

Tomkins et al. (2017) Reviewer bias in single-versus double-blind peer review. PNAS 114:12708-12713.

Essential revisions

1. The manuscript, as it stands, does not bring much more to the topic than what has been published in previous papers, such as Solans-Domenech et al., 2017, which analyzed a larger sample (and is not cited by the authors – see point 4 below). In order to improve the manuscript I would advise the authors to expand the scope as follows:

1a. The option and justification given by the authors of not studying the effect of gender is not convincing. Although applicants were not asked about their gender before 2021, it could still be possible to perform an analysis based on a proxy where first names would be associated to gender.

The analysis on gender distribution in the LOI, Full Application invitations, and Program Awards before and after blinding is included in the revision. In collecting this data, we found that there was no evidence of gender bias in the Full Application invitations or Program Awards either before or after blinding.

1b. The following point is optional: It would be interesting to check the final outcome of the evaluation, at the second step, when the proposal is not blinded anymore, and to assess if the final set of successful candidates (in terms of affiliations) differ from those before the blinding step was introduced.

We extended our analysis to include Program Awards based on institutional affiliation and included the results in the discussion.

2. The prestige ranking is based on total funding size, which is certainly correlated with the prestige of the institutions. However, this measure is also highly correlated with the size of the institutions, which might not accurately reflect the prestige: for example, Harvard, Yale and MIT are in the second tier; Chicago, Berkeley and Caltech are in the third tier; and there are also some very prestigious institutions (such as Rockefeller University, Fred Hutchinson Cancer Research Center, and the Mayo Clinic) in the bottom tier.

This point could be addressed by using a modified prestige ranking obtained by normalizing the funding size with respect to the size of the institutions (e.g., the number of researchers) might be more appropriate.

Another approach would be to use other university rankings (eg Shanghai, THE, Leiden, etc).

Our suggestion is that several different proxies for prestige should be used in the study (eg two or more of NSF, NSF normalized, Shanghai, THE, Leiden, and maybe even the proxy based on previous performance when applying to Beckman), and that the results of all the proxies studied should be reported.

We implemented the suggestion presented here from the reviewers and expanded our analysis to include eight different lists of institutional ranks, as well as then developed a “Consensus List” based on reviewing the trends and differences between these lists. The analysis results using all nine of these lists are included in the revised manuscript.

With regards to normalizing the NCSES Federal funding data by institution size, we did consult with the program team at NSF NCSES to ask if they also collect data that could help with this analysis. While one of their surveys they send to institutions annually (the HERD survey) does include a question about the number of faculty engaged in scientific research, they commented that the compliance with response to this question is lower than usual, and not all of the institutions on the top 100 list would be included in this HERD survey (private communication). With the inclusion of the other university rankings, we did not pursue this question about normalization any further.

3. There are a number of points regarding the statistical analysis that need to be addressed

3a. Regarding the NSF ranking: the 2018 data has been used, but as the study covers 8 years, the authors should at least have tested if rankings from different years would give different results.

We included additional years of rankings in our study and discussion.

3b. The division of prestige categories into four groups based on equal partition of total national funding, while reasonable, still seems a bit arbitrary. It might be of interest to test the robustness of the results with respect to the number of categories chosen.

We respectfully disagree that this division was based only on the concentration of funding received at the top institutions, although that is a real effect that is reported in the references cited. The number of categories was also based on the division of our LOIs into these categories (see Table 6 in the Methods section). We also added Supplemental Table 3 with the full data set of number of LOIs, Full Application invitations and Program Awards by institution to help the reader see how the applications are distributed among the institutions each year.

3c. The sample size in the treatment group (data from 2021-2023) is substantially smaller than the sample size in the control group (data from 2016-2020). Therefore, the fact that the p-value becomes insignificant might simply be because of reduced sample size instead of reduced prestige bias. Down-sampling the institutions for 2016-2020 might make the comparison more fair. Or alternatively, a difference-in-difference design might be able to show the effect more clearly.

For this revision, we were able to include an additional year of blinded review analysis, and we balanced the study with 4 years of unblinded and 4 years of blinded data. We did still receive a smaller number of LOIs during some of the blinded study years (which we attribute to COVID disruptions). We considered this difference in sample size in developing the “Relative Advantage” metric that we used for our analysis. If you down-sample the LOIs received in the unblinded years across the Institutional Categories, the “Relative Advantage” ratio remains the same.

4. The discussion of the existing literature on bias in research grant evaluation needs to be improved. I recommend that the authors cite and discuss the following articles:

Forscher, P.S., Cox, W.T.L., Brauer, M. et al. (2019) Little race or gender bias in an experiment of initial review of NIH R01 grant proposals. Nat Hum Behav 3:257-264.

Marsh et al. (2008) Improving the peer-review process for grant applications: Reliability, validity, bias, and generalizability. American Psychologist, 63:160-168.

Solans-Domenech et al. (2017) Blinding applicants in a first-stage peer-review process of biomedical research grants: An observational study. Res. Eval. 26:181-189. DOI:10.1093/reseval/rvx021

I also recommend that the authors cite and discuss the following article about prestige bias in the peer review of papers submitted to a conference:

Tomkins et al. (2017) Reviewer bias in single-versus double-blind peer review. PNAS 114:12708-12713.

Thank you for the additional references, which have been consulted and included in the revised manuscript.

Author details

1. Anne E Hultgren

   Anne E Hultgren is at the Arnold and Mabel Beckman Foundation, Irvine, United States

   Contribution

   Conceptualization, Data curation, Methodology, Writing – original draft

   For correspondence

   ahultgren@beckman-foundation.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0001-8266-1050

2. Nicole MF Patras

   Nicole MF Patras is at the Arnold and Mabel Beckman Foundation, Irvine, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0008-6229-0038

3. Jenna Hicks

   Jenna Hicks is at the Health Research Alliance, Research Park, United States

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4486-9926

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