Using theoretical constraints and the TASI taxonomy to delineate predictably replicable findings
The focus of the present article is not on failures to replicate but on the more optimistically framed and more fruitful question: What stable findings can be reproduced reliably and can be trusted by decision makers, managers, health agents, or politicians? We propagate the working hypothesis that...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
27 June 2024
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| In: |
Psychonomic bulletin & review
Year: 2024, Volume: 31, Issue: 6, Pages: 2581-2598 |
| ISSN: | 1531-5320 |
| DOI: | 10.3758/s13423-024-02521-4 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3758/s13423-024-02521-4
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| Author Notes: | Klaus Fiedler, David Trafimow |
| Summary: | The focus of the present article is not on failures to replicate but on the more optimistically framed and more fruitful question: What stable findings can be reproduced reliably and can be trusted by decision makers, managers, health agents, or politicians? We propagate the working hypothesis that a twofold key to stable and replicable findings lies in the existence of theoretical constraints and, no less important, in researchers’ sensitivity to metatheoretical, auxiliary assumptions. We introduce a hierarchy of four levels of theoretical constraints—a priori principles, psychophysical, empirical, and modelling constraints—combined with the TASI taxonomy of theoretical, auxiliary, statistical, and inferential assumptions Trafimow, Journal for the Theory of Social Behaviour, 52, 37-48, (2022). Although theoretical constraints clearly facilitate stable and replicable research findings, TASI reminds us of various reasons why even perfectly valid hypotheses need not always be borne out. The presented framework should help researchers to operationalize conditions under which theoretical constraints render empirical findings most predictable. |
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| Item Description: | Gesehen am 29.01.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1531-5320 |
| DOI: | 10.3758/s13423-024-02521-4 |