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Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance

There is a norm in psychology to use causally ambiguous statistical language, rather than straightforward causal language, when describing methods and results of nonexperimental studies. However, causally ambiguous language may inhibit a critical examination of the study’s causal assumptions and lea...

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Autores principales: Alvarez-Vargas, Daniela, Braithwaite, David, Lortie-Forgues, Hugues, Moore, Melody, Wan, Sirui, Martin, Elizabeth, Bailey, Drew Hal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602341/
https://www.ncbi.nlm.nih.gov/pubmed/37883517
http://dx.doi.org/10.1371/journal.pone.0286403
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author Alvarez-Vargas, Daniela
Braithwaite, David
Lortie-Forgues, Hugues
Moore, Melody
Wan, Sirui
Martin, Elizabeth
Bailey, Drew Hal
author_facet Alvarez-Vargas, Daniela
Braithwaite, David
Lortie-Forgues, Hugues
Moore, Melody
Wan, Sirui
Martin, Elizabeth
Bailey, Drew Hal
author_sort Alvarez-Vargas, Daniela
collection PubMed
description There is a norm in psychology to use causally ambiguous statistical language, rather than straightforward causal language, when describing methods and results of nonexperimental studies. However, causally ambiguous language may inhibit a critical examination of the study’s causal assumptions and lead to a greater acceptance of policy recommendations that rely on causal interpretations of nonexperimental findings. In a preregistered experiment, 142 psychology faculty, postdocs, and doctoral students (54% female), ages 22–67 (M = 33.20, SD = 8.96), rated the design and analysis from hypothetical studies with causally ambiguous statistical language as of higher quality (by .34-.80 SD) and as similarly or more supportive (by .16-.27 SD) of policy recommendations than studies described in straightforward causal language. Thus, using statistical rather than causal language to describe nonexperimental findings did not decrease, and may have increased, perceived support for implicitly causal conclusions.
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spelling pubmed-106023412023-10-27 Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance Alvarez-Vargas, Daniela Braithwaite, David Lortie-Forgues, Hugues Moore, Melody Wan, Sirui Martin, Elizabeth Bailey, Drew Hal PLoS One Research Article There is a norm in psychology to use causally ambiguous statistical language, rather than straightforward causal language, when describing methods and results of nonexperimental studies. However, causally ambiguous language may inhibit a critical examination of the study’s causal assumptions and lead to a greater acceptance of policy recommendations that rely on causal interpretations of nonexperimental findings. In a preregistered experiment, 142 psychology faculty, postdocs, and doctoral students (54% female), ages 22–67 (M = 33.20, SD = 8.96), rated the design and analysis from hypothetical studies with causally ambiguous statistical language as of higher quality (by .34-.80 SD) and as similarly or more supportive (by .16-.27 SD) of policy recommendations than studies described in straightforward causal language. Thus, using statistical rather than causal language to describe nonexperimental findings did not decrease, and may have increased, perceived support for implicitly causal conclusions. Public Library of Science 2023-10-26 /pmc/articles/PMC10602341/ /pubmed/37883517 http://dx.doi.org/10.1371/journal.pone.0286403 Text en © 2023 Alvarez-Vargas et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Alvarez-Vargas, Daniela
Braithwaite, David
Lortie-Forgues, Hugues
Moore, Melody
Wan, Sirui
Martin, Elizabeth
Bailey, Drew Hal
Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
title Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
title_full Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
title_fullStr Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
title_full_unstemmed Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
title_short Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance
title_sort hedges, mottes, and baileys: causally ambiguous statistical language can increase perceived study quality and policy relevance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602341/
https://www.ncbi.nlm.nih.gov/pubmed/37883517
http://dx.doi.org/10.1371/journal.pone.0286403
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