A1 Journal article (refereed)
Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences (2020)
Christie, A. P., Abecasis, D., Adjeroud, M., Alonso, J. C., Amano, T., Anton, A., Baldigo, B. P., Barrientos, R., Bicknell, J. E., Buhl, D. A., Cebrian, J., Ceia, R. S., Cibils-Martina, L., Clarke, S., Claudet, J., Craig, M. D., Davoult, D., De Backer, A., Donovan, M. K., . . . Sutherland, W. J. (2020). Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nature Communications, 11, Article 6377. https://doi.org/10.1038/s41467-020-20142-y
JYU authors or editors
Publication details
All authors or editors: Christie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan C.; Amano, Tatsuya; Anton, Alvaro; Baldigo, Barry P.; Barrientos, Rafael; Bicknell, Jake E.; Buhl, Deborah A.; et al.
Journal or series: Nature Communications
eISSN: 2041-1723
Publication year: 2020
Volume: 11
Article number: 6377
Publisher: Nature Publishing Group
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1038/s41467-020-20142-y
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73343
Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
Keywords: environmental sciences; social sciences; methodology; research methods; reliability (general); randomised controlled trials; political decision making; evidence-based practices
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 3