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Does the pressure to fill journal quotas bias evaluation?: Evidence from publication delays and rejection rates.

ABSTRACT: Although the peer review system of academic journals is seen as fundamental to scientific achievement, a major threat to the validity of the system is a potential evaluation bias resulting from constraints at the journal level. In this study, we examine how the time pressure to maintain a fixed periodical quota for journal publication can influence a journal editor's decision to accept or reject a paper at any given point in time. We find that an increase in publication backlog, proxied as the average delay between paper acceptance and print publication, is correlated with an increase in the subsequent rejection rates of new submissions. Our findings suggest that time pressures inherent in the peer review system may be a source of potential evaluator bias, calling for a need to reconsider the current quota system.

SUBMITTER: Park B

PROVIDER: S-EPMC7418967 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Does the pressure to fill journal quotas bias evaluation?: Evidence from publication delays and rejection rates.

Park Brian B Sohn Eunhee E Kim Soohun S

PloS one 20200811 8

Although the peer review system of academic journals is seen as fundamental to scientific achievement, a major threat to the validity of the system is a potential evaluation bias resulting from constraints at the journal level. In this study, we examine how the time pressure to maintain a fixed periodical quota for journal publication can influence a journal editor's decision to accept or reject a paper at any given point in time. We find that an increase in publication backlog, proxied as the a ...[more]

PMID: 32780771

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Project description:Selective publication and reporting in individual papers compromise the scientific record, but are meta-analyses as compromised as their constituent studies? We systematically sampled 63 meta-analyses (each comprising at least 40 studies) in PLoS One, top medical journals, top psychology journals, and Metalab, an online, open-data database of developmental psychology meta-analyses. We empirically estimated publication bias in each, including only the peer-reviewed studies. Across all meta-analyses, we estimated that "statistically significant" results in the expected direction were only 1.17 times more likely to be published than "nonsignificant" results or those in the unexpected direction (95% CI: [0.93, 1.47]), with a confidence interval substantially overlapping the null. Comparable estimates were 0.83 for meta-analyses in PLoS One, 1.02 for top medical journals, 1.54 for top psychology journals, and 4.70 for Metalab. The severity of publication bias did differ across individual meta-analyses; in a small minority (10%; 95% CI: [2%, 21%]), publication bias appeared to favor "significant" results in the expected direction by more than threefold. We estimated that for 89% of meta-analyses, the amount of publication bias that would be required to attenuate the point estimate to the null exceeded the amount of publication bias estimated to be actually present in the vast majority of meta-analyses from the relevant scientific discipline (exceeding the 95th percentile of publication bias). Study-level measures ("statistical significance" with a point estimate in the expected direction and point estimate size) did not indicate more publication bias in higher-tier versus lower-tier journals, nor in the earliest studies published on a topic versus later studies. Overall, we conclude that the mere act of performing a meta-analysis with a large number of studies (at least 40) and that includes non-headline results may largely mitigate publication bias in meta-analyses, suggesting optimism about the validity of meta-analytic results.

Project description:Background:Publication bias is a form of scientific misconduct. It threatens the validity of research results and the credibility of science. Although several tests on publication bias exist, no in-depth evaluations are available that examine which test performs best for different research settings. Methods:Four tests on publication bias, Egger's test (FAT), p-uniform, the test of excess significance (TES), as well as the caliper test, were evaluated in a Monte Carlo simulation. Two different types of publication bias and its degree (0%, 50%, 100%) were simulated. The type of publication bias was defined either as file-drawer, meaning the repeated analysis of new datasets, or p-hacking, meaning the inclusion of covariates in order to obtain a significant result. In addition, the underlying effect (β = 0, 0.5, 1, 1.5), effect heterogeneity, the number of observations in the simulated primary studies (N = 100, 500), and the number of observations for the publication bias tests (K = 100, 1,000) were varied. Results:All tests evaluated were able to identify publication bias both in the file-drawer and p-hacking condition. The false positive rates were, with the exception of the 15%- and 20%-caliper test, unbiased. The FAT had the largest statistical power in the file-drawer conditions, whereas under p-hacking the TES was, except under effect heterogeneity, slightly better. The CTs were, however, inferior to the other tests under effect homogeneity and had a decent statistical power only in conditions with 1,000 primary studies. Discussion:The FAT is recommended as a test for publication bias in standard meta-analyses with no or only small effect heterogeneity. If two-sided publication bias is suspected as well as under p-hacking the TES is the first alternative to the FAT. The 5%-caliper test is recommended under conditions of effect heterogeneity and a large number of primary studies, which may be found if publication bias is examined in a discipline-wide setting when primary studies cover different research problems.

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Does the pressure to fill journal quotas bias evaluation?: Evidence from publication delays and rejection rates.

Publications

Does the pressure to fill journal quotas bias evaluation?: Evidence from publication delays and rejection rates.

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