"How readers understand causal and correlational expressions used in news headlines": Correction to Adams et al. (2016)

Abstract

Reports an error in "How Readers Understand Causal and Correlational Expressions Used in News Headlines" by Rachel C. Adams, Petroc Sumner, Solveiga Vivian-Griffiths, Amy Barrington, Andrew Williams, Jacky Boivin, Christopher D. Chambers and Lewis Bott (Journal of Experimental Psychology: Applied, Advanced Online Publication, Nov 3, 2016, np). In the article, the fourth author was inadvertently omitted from the advance online version. Also, the second paragraph of the author note should have included the following: "Amy Barrington contributed to the design and data collection for Experiments 2 and 3. We thank the following undergraduate students for contributions to Experiment 1 and pilot work leading up to the project: Laura Benjamin, Cecily Donnelly, Cameron Dunlop, Rebecca Emerson, Rose Fisher, Laura Jones, Olivia Manship, Hannah McCarthy, Naomi Scott, Eliza Walwyn-Jones, Leanne Whelan, and Joe Wilton." All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-52933-001.) Science-related news stories can have a profound impact on how the public make decisions. The current study presents 4 experiments that examine how participants understand scientific expressions used in news headlines. The expressions concerned causal and correlational relationships between variables (e.g., "being breast fed makes children behave better"). Participants rated or ranked headlines according to the extent that one variable caused the other. Our results suggest that participants differentiate between 3 distinct categories of relationship: direct cause statements (e.g., "makes," "increases"), which were interpreted as the most causal; can cause statements (e.g., "can make," "can increase"); and moderate cause statements (e.g., "might cause," "linked," "associated with"), but do not consistently distinguish within the last group despite the logical distinction between cause and association. On the basis of this evidence, we make recommendations for appropriately communicating cause and effect in news headlines. (PsycINFO Database Record