Exploring a formal approach to selecting studies for replication: A feasibility study in social neuroscience

Cortex. 2024 Feb:171:330-346. doi: 10.1016/j.cortex.2023.10.012. Epub 2023 Nov 7.

Abstract

Replication of published results is crucial for ensuring the robustness and self-correction of research, yet replications are scarce in many fields. Replicating researchers will therefore often have to decide which of several relevant candidates to target for replication. Formal strategies for efficient study selection have been proposed, but none have been explored for practical feasibility - a prerequisite for validation. Here we move one step closer to efficient replication study selection by exploring the feasibility of a particular selection strategy that estimates replication value as a function of citation impact and sample size (Isager, van 't Veer, & Lakens, 2021). We tested our strategy on a sample of fMRI studies in social neuroscience. We first report our efforts to generate a representative candidate set of replication targets. We then explore the feasibility and reliability of estimating replication value for the targets in our set, resulting in a dataset of 1358 studies ranked on their value of prioritising them for replication. In addition, we carefully examine possible measures, test auxiliary assumptions, and identify boundary conditions of measuring value and uncertainty. We end our report by discussing how future validation studies might be designed. Our study demonstrates the importance of investigating how to implement study selection strategies in practice. Our sample and study design can be extended to explore the feasibility of other formal study selection strategies that have been proposed.

Keywords: Bibliometric analysis; Expected utility; Exploratory report; Replication; Replication value; Social neuroscience.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cognitive Neuroscience*
  • Feasibility Studies
  • Humans
  • Reproducibility of Results
  • Research Design
  • Uncertainty