The benefits of preregistration and Registered Reports

Lakens, Daniël; Mesquida, Cristian; Rasti, Sajedeh; Ditroilo, Massimiliano

doi:10.1080/2833373x.2024.2376046

Evidence-Based Toxicology

2024

DOI: 10.1080/2833373x.2024.2376046

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The benefits of preregistration and Registered Reports

Daniël Lakens,

Cristian Mesquida,

Sajedeh Rasti

et al.

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2024

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Cited by 2 publications

References 130 publications

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Preregistration and predictivism

Choi

2024

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Preregistration and predictivism

Choi

2024

Synthese

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How large must an associational mean difference be to support a causal effect?

Höfler,

Pronizius,

Buchanan

2024

Methodology

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An observational study might support a causal claim if the association found cannot be explained by bias due to unconsidered confounders. This bias depends on how strongly the common predisposition, a summary of unconsidered confounders, is related to the factor and the outcome. For a positive effect to be supported, the product of these two relations must be smaller than the left boundary of the confidence interval for, e.g., a standardised mean difference (d). We suggest means to derive heuristics for how large this product must be to serve as a confirmatory threshold. We also provide non-technical, visual means to express researchers’ assumptions on the two relations to assess whether a finding on d is explainable by omitted confounders. The ViSe tool, available as an R package and Shiny application, allows users to choose between various effect sizes and apply it to their own data or published summary results.

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The benefits of preregistration and Registered Reports

Cited by 2 publications

References 130 publications

Preregistration and predictivism

Preregistration and predictivism

How large must an associational mean difference be to support a causal effect?

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