A comment on Arechar et al.'s (2023)" Understanding and combatting misinformation across 16 countries on six continents"

December 15, 2025·
Elen Le Foll
In alphabetical order
Anna Yi Leung
Anna Yi Leung
In alphabetical order
,
Désirée Nießen
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,
Caroline Poppa
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· 0 min read
Abstract
In this commentary, we report the results of our computational reproduction of the main claims from Arechar et al. (2023), which examined psychological and social predictors of susceptibility to COVID-19 misinformation across 16 countries. Using the dataset and replication materials provided by the authors, we attempted to reproduce the study’s findings using Stata (based on the authors’ analysis script) and R (by translating and reconstructing the code). To assess computational reproducibility, we ran the original Stata code and independently reimplemented key analyses in R. While the Stata script allowed reproduction of several key statistical outputs, it lacked plotting code for most figures and required code edits for compatibility with Version 18. In contrast, our R implementation successfully reproduced Figures 2, 3, 4A-C, 5A-D, and 6 with visual and numerical results closely resembling those in the original publication. We also compared the pre-analysis plan and the article. We found inconsistencies in the specificity of the research questions, analysis scope, and terminology, making it challenging to assess fidelity to the preregistration. Overall, our results highlight that, while many of the reported statistical findings are robust to reanalysis, the study’s graphical reproducibility could be improved by providing the complete code for all figures, better documentation, and ensuring greater consistency across the replication packages.
Type
Publication
I4R Discussion Paper Series No. 277, Institute for Replication (I4R)
publications
Anna Yi Leung
Authors
Research Scientist in Psycholinguistics and Metascience
I am a doctoral researcher specialising in psycholinguistics and reading development. My work explores the cognitive mechanisms of how we learn to read, with a focus on subtyping dyslexia to provide personalised support. Committed to metascience, I integrate open science practices to ensure my research is robust and transparent. Beyond the lab, I am passionate about bridging the gap between science and education. Through Open Education initiatives, I translate complex linguistic theories into practical, evidence-based resources for researchers, teachers, and clinicians. I am dedicated to empowering the global community, supporting diverse learners in our unique and vibrant multilingual environment.