Margoni, F. and Walkinshaw, N. orcid.org/0000-0003-2134-6548 (2024) Subjective logic as a complementary tool to meta-analysis to explicitly address second-order uncertainty in research findings: a case from infant studies. Infant Behavior and Development, 76. 101978. ISSN 0163-6383
Abstract
Any experiment brings about results and conclusions that necessarily have a component of uncertainty. Many factors influence the degree of this uncertainty, yet they can be overlooked when drawing conclusions from a body of research. Here, we showcase how subjective logic could be employed as a complementary tool to meta-analysis to incorporate the chosen sources of uncertainty into the answer that researchers seek to provide to their research question. We illustrate this approach by focusing on a body of research already meta-analyzed, whose overall aim was to assess if human infants prefer prosocial agents over antisocial agents. We show how each finding can be encoded as a subjective opinion, and how findings can be aggregated to produce an answer that explicitly incorporates uncertainty. We argue that a core feature and strength of this approach is its transparency in the process of factoring in uncertainty and reasoning about research findings. Subjective logic promises to be a powerful complementary tool to incorporate uncertainty explicitly and transparently in the evaluation of research.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | subjective logic; meta-analysis; uncertainty; replication; infancy research |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/T030526/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jul 2024 09:27 |
Last Modified: | 09 Aug 2024 15:10 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.infbeh.2024.101978 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:214722 |