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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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Detalles Bibliográficos
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
Descripción
Sumario: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.