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Title: Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
Authors: 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.
Cebrian, Just
Ceia, Ricardo 
Cibils-Martina, Luciana
Clarke, Sarah
Claudet, Joachim
Craig, Michael D
Davoult, Dominique
De Backer, Annelies
Donovan, Mary K.
Eddy, Tyler D.
França, Filipe M.
Gardner, Jonathan P. A.
Harris, Bradley P.
Huusko, Ari
Jones, Ian L.
Kelaher, Brendan P.
Kotiaho, Janne S.
López-Baucells, Adrià
Major, Heather L.
Mäki-Petäys, Aki
Martín, Beatriz
Martín, Carlos A.
Martin, Philip A.
Mateos-Molina, Daniel
McConnaughey, Robert A.
Meroni, Michele
Meyer, Christoph F. J.
Mills, Kade
Montefalcone, Monica
Noreika, Norbertas
Palacín, Carlos
Pande, Anjali
Pitcher, C. Roland
Ponce, Carlos
Rinella, Matt
Rocha, Ricardo António da Silva 
Ruiz-Delgado, María C.
Schmitter-Soto, Juan J.
Shaffer, Jill A.
Sharma, Shailesh
Sher, Anna A.
Stagnol, Doriane
Stanley, Thomas R.
Stokesbury, Kevin D. E.
Torres, Aurora
Tully, Oliver
Vehanen, Teppo
Watts, Corinne
Zhao, Qingyuan
Sutherland, William J.
Issue Date: 11-Dec-2020
Publisher: Springer Nature
Serial title, monograph or event: Nature Communications
Volume: 11
Issue: 1
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.
ISSN: 2041-1723
DOI: 10.1038/s41467-020-20142-y
Rights: openAccess
Appears in Collections:I&D MARE - Artigos em Revistas Internacionais
I&D CFE - Artigos em Revistas Internacionais

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