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Predicting the replicability of social science lab experiments
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutet för social forskning (SOFI). Stockholm School of Economics, Sweden.
Vise andre og tillknytning
Rekke forfattare: 92019 (engelsk)Inngår i: PLoS ONE, E-ISSN 1932-6203, Vol. 14, nr 12, artikkel-id e0225826Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We measure how accurately replication of experimental results can be predicted by black-box statistical models. With data from four large-scale replication projects in experimental psychology and economics, and techniques from machine learning, we train predictive models and study which variables drive predictable replication. The models predicts binary replication with a cross-validated accuracy rate of 70% (AUC of 0.77) and estimates of relative effect sizes with a Spearman ρ of 0.38. The accuracy level is similar to market-aggregated beliefs of peer scientists [1, 2]. The predictive power is validated in a pre-registered out of sample test of the outcome of [3], where 71% (AUC of 0.73) of replications are predicted correctly and effect size correlations amount to ρ = 0.25. Basic features such as the sample and effect sizes in original papers, and whether reported effects are single-variable main effects or two-variable interactions, are predictive of successful replication. The models presented in this paper are simple tools to produce cheap, prognostic replicability metrics. These models could be useful in institutionalizing the process of evaluation of new findings and guiding resources to those direct replications that are likely to be most informative.

sted, utgiver, år, opplag, sider
2019. Vol. 14, nr 12, artikkel-id e0225826
HSV kategori
Identifikatorer
URN: urn:nbn:se:su:diva-178421DOI: 10.1371/journal.pone.0225826OAI: oai:DiVA.org:su-178421DiVA, id: diva2:1388901
Tilgjengelig fra: 2020-01-28 Laget: 2020-01-28 Sist oppdatert: 2020-01-28bibliografisk kontrollert

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