Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences



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Christie, Alec P. and Abecasis, David and Adjeroud, Mehdi and Alonso, Juan C. and Amano, Tatsuya and Anton, Álvaro and Baldigo, Barry P. and Barrientos Yuste, Rafael and Bicknell, Jack E. and Buhl, Deborah A. and Cebrian, Just and Ceia, Ricardo S. and Cibils-Martina, Ricardo and Clarke, Sarah and Claudet, Joachim and Craig, Michael D. and Davoult, Dominique and De Backer, Annelies and Donovan, Mary K. and Eddy, Tyler D. and França, Filipe M. and Gardner, Jonathan P. A. and Harris, Bradley P. and Huusko, Ari and Jones, Ian L. and Kelaher, Brendan P. and Kotiaho, Janne S. and López-Baucells, Adrià and Major, Heather L. and Mäki-Petäys, Aki and Martín, Beatriz and Martín, Carlos A. and Martin, Philip A. and Mateos-Molina, Daniel and McConnaughey, Robert A. and Meyer, Christoph F. J. and Mills, Kade and Montefalcone, Monica and Noreika, Norbertas and Palacín, Carlos and Pande, Anjali and Pitcher, C. Roland and Ponce, Carlos and Rinella, Matt and Rocha, Ricardo and Ruiz-Delgado, María C. and Schmitter-Soto, Juan J. and Shaffer, Jill A. and Sharma, Shailesh and Sher, Anna A. and Stagnol, Doriane and Stanley, Thomas R. and Stokesbury, Kevin D. E. and Torres, Aurora and Tully, Oliver and Vehanen, Teppo and Watts, Corinne and Zhao, Quingyuan and Sutherland, William J. (2020) Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nature Communications, 11 (6377). pp. 1-11. ISSN Electronic: 2041-1723

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Official URL: https://www.nature.com/articles/s41467-020-20142-y


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.

Item Type:Article
Uncontrolled Keywords:Bias, Biodiversity; Environment; Social sciences
Subjects:Medical sciences > Biology > Ecology
Medical sciences > Biology > Environment
ID Code:64887
Deposited On:16 Apr 2021 08:44
Last Modified:20 Apr 2021 10:01

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