Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process - PubMed (original) (raw)

. 2023 Jan 26;18(1):e0274429.

doi: 10.1371/journal.pone.0274429. eCollection 2023.

Martin Bush 1, Bonnie C Wintle 1 2, Fallon Mody 1, Eden T Smith 1, Anca M Hanea 1 3, Elliot Gould 1 2 3, Victoria Hemming 1 4, Daniel G Hamilton 1, Libby Rumpff 1 2, David P Wilkinson 1 2, Ross Pearson 1, Felix Singleton Thorn 1, Raquel Ashton 1, Aaron Willcox 1, Charles T Gray 1 5, Andrew Head 1, Melissa Ross 1, Rebecca Groenewegen 1 2, Alexandru Marcoci 6, Ans Vercammen 7 8, Timothy H Parker 9, Rink Hoekstra 10, Shinichi Nakagawa 11, David R Mandel 12, Don van Ravenzwaaij 10, Marissa McBride 7, Richard O Sinnott 13, Peter Vesk 1 2, Mark Burgman 7, Fiona Fidler 1

Affiliations

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process

Hannah Fraser et al. PLoS One. 2023.

Abstract

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

Copyright: © 2023 Fraser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1

Fig 1. Overview of the IDEA protocol, as adopted in the repliCATS project.

Fig 2

Fig 2. Summary of results from validation experiment.

Each dot represents a particular group’s aggregated assessment of replicability for each claim, with five groups assessing all 25 claims drawn from previous replication projects. Claims for which the replication study did not produce a statistically significant result are shown in the top pane, and claims for which the replication study did produce a statistically significant result are shown in the bottom pane.

Fig 3

Fig 3. The repliCATS platform.

Anticlockwise from centre top (a) complete layout, plus expanded details of: (b) claim information; (c) and (d) responses to elicitation and aggregated feedback; and (e) participants’ reasoning comments.

References

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