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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 Carlos
Amano, Tatsuya
Anton, Alvaro
Baldigo, Barry Paul
Barrientos, Rafael
Bicknell, Jake E.
Buhl, Deborah A.
Cebrian, Just
Ceia, Ricardo Santos
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
Harris, Bradley P.
Huusko, Ari
Jones, Ian L.
Kelaher, Brendan P.
Kotiaho, J. 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, Matthew James
Rocha, Ricardo
Ruiz-Delgado, María C.
Schmitter-Soto, Juan Jacobo
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 H.
Zhao, Qingyuan
Sutherland, William J.
Keywords: Biodiversity
Data Set
Decision Making
Numerical Model
Conservation Biology
intervention study
randomized controlled trial (topic)
Issue Date: 2020
metadata.dc.publisher.journal: Nature Communications
metadata.dc.relation.ispartof: Volume 11, Número 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. © 2020, The Author(s).
metadata.dc.identifier.doi: 10.1038/s41467-020-20142-y
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