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

Autor: Daniela Alvarez-Vargas, David Braithwaite, Hugues Lortie-Forgues, Melody Moore, Sirui Wan, Elizabeth Martin, Drew Hal Bailey
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: PLoS ONE, Vol 18, Iss 10, p e0286403 (2023)
Druh dokumentu: article
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0286403
Popis: 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.
Databáze: Directory of Open Access Journals
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