2019Unpublished venueOpen access

Comparing Meta-Analyses and Pre-Registered Multiple Labs Replication Projects

Amanda Kvarven, Eirik Strømland, Magnus Johannesson

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Abstract

Many researchers rely on meta-analysis to summarize research evidence. However, recent replication projects in the behavioral sciences suggest that effect sizes of original studies are overestimated, and this overestimation is typically attributed to publication bias and selective reporting of scientific results. As the validity of meta-analyses depends on the primary studies, there is a concern that systematic overestimation of effect sizes may translate into biased meta-analytic effect sizes. We compare the results of meta-analyses to large-scale pre-registered replications in psychology carried out at multiple labs. The multiple labs replications provide relatively precisely estimated effect sizes, which do not suffer from publication bias or selective reporting. Searching the literature, 17 meta-analyses – spanning more than 1,200 effect sizes and more than 370,000 participants - on the same topics as multiple labs replications are identified. We find that the meta-analytic effect sizes are significantly different from the replication effect sizes for 12 out of the 17 meta-replication pairs. These differences are systematic and on average meta-analytic effect sizes are about three times as large as the replication effect sizes.

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What this paper is about

Many researchers rely on meta-analysis to summarize research evidence. However, recent replication projects in the behavioral sciences suggest that effect sizes of original studies are overestimated, and this overestimation is typically attributed to publication bias and selective reporting of scientific results. As the validity of meta-analyses depends on the primary studies, there is a concern that systematic overestimation of effect sizes may translate into biased meta-analytic effect sizes. We compare the results of meta-analyses to large-scale pre-registered replications in psychology carried out at multiple labs. The multiple labs replications provide relatively precisely estimated effect sizes, which do not suffer from publication bias or selective reporting. Searching the literature, 17 meta-analyses – spanning more than 1,200 effect sizes and more than 370,000 participants - on the same topics as multiple labs replications are identified. We find that the meta-analytic effect sizes are significantly different from the replication effect sizes for 12 out of the 17 meta-replication pairs. These differences are systematic and on average meta-analytic effect sizes are about three times as large as the replication effect sizes.

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Available abstract

Many researchers rely on meta-analysis to summarize research evidence. However, recent replication projects in the behavioral sciences suggest that effect sizes of original studies are overestimated, and this overestimation is typically attributed to publication bias and selective reporting of scientific results. As the validity of meta-analyses depends on the primary studies, there is a concern that systematic overestimation of effect sizes may translate into biased meta-analytic effect sizes. We compare the results of meta-analyses to large-scale pre-registered replications in psychology carried out at multiple labs. The multiple labs replications provide relatively precisely estimated effect sizes, which do not suffer from publication bias or selective reporting. Searching the literature, 17 meta-analyses – spanning more than 1,200 effect sizes and more than 370,000 participants - on the same topics as multiple labs replications are identified. We find that the meta-analytic effect sizes are significantly different from the replication effect sizes for 12 out of the 17 meta-replication pairs. These differences are systematic and on average meta-analytic effect sizes are about three times as large as the replication effect sizes.

Key concepts: Replication (statistics), Meta-analysis, Publication bias, Psychology, Systematic review, Statistics, Computer science, Econometrics

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