RT Journal Article
T1 Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
A1 Christie, Alec P.
A1 Abecasis, David
A1 Adjeroud, Mehdi
A1 Alonso, Juan C.
A1 Amano, Tatsuya
A1 Anton, Álvaro
A1 Baldigo, Barry P.
A1 Barrientos Yuste, Rafael
A1 Bicknell, Jack E.
A1 Buhl, Deborah A.
A1 Cebrian, Just
A1 Ceia, Ricardo S.
A1 Cibils-Martina, Ricardo
A1 Clarke, Sarah
A1 Claudet, Joachim
A1 Craig, Michael D.
A1 Davoult, Dominique
A1 De Backer, Annelies
A1 Donovan, Mary K.
A1 Eddy, Tyler D.
A1 França, Filipe M.
A1 Gardner, Jonathan P. A.
A1 Harris, Bradley P.
A1 Huusko, Ari
A1 Jones, Ian L.
A1 Kelaher, Brendan P.
A1 Kotiaho, Janne S.
A1 López-Baucells, Adrià
A1 Major, Heather L.
A1 Mäki-Petäys, Aki
A1 Martín, Beatriz
A1 Martín De La Calle, Carlos Alfonso
A1 Martin, Philip A.
A1 Mateos-Molina, Daniel
A1 McConnaughey, Robert A.
A1 Meyer, Christoph F. J.
A1 Mills, Kade
A1 Montefalcone, Monica
A1 Noreika, Norbertas
A1 Palacín, Carlos
A1 Pande, Anjali
A1 Pitcher, C. Roland
A1 Ponce, Carlos
A1 Rinella, Matt
A1 Rocha, Ricardo
A1 Ruiz-Delgado, María C.
A1 Schmitter-Soto, Juan J.
A1 Shaffer, Jill A.
A1 Sharma, Shailesh
A1 Sher, Anna A.
A1 Stagnol, Doriane
A1 Stanley, Thomas R.
A1 Stokesbury, Kevin D. E.
A1 Torres, Aurora
A1 Tully, Oliver
A1 Vehanen, Teppo
A1 Watts, Corinne
A1 Zhao, Quingyuan
A1 Sutherland, William J.
AB 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.
PB Nature Research
SN Electronic: 2041-1723
YR 2020
FD 2020-12-11
LK https://hdl.handle.net/20.500.14352/8004
UL https://hdl.handle.net/20.500.14352/8004
LA eng
NO Ministerio de Ciencia, Innovación y Universidades (MCIU)
NO Comunidad de Madrid
NO Foundation for Science and Technology (Portugal)
NO Madeira’s Regional Agency for the Development of Research, Technology and Innovation (ARDITI)
NO Natural Environment Research Council via Cambridge Earth System (NERC DTP)
NO Kenneth Miller Trust and Australian Research Council
NO Arcadia, MAVA, and The David and Claudia Harding Foundation
NO Grantham Foundation for the Protection of the Environment, Kenneth Miller Trust and Australian Research Council Future Fellowship
NO Alabama Department of Conservation and Natural Resources
NO Ministry of Business, Innovation and Employment (New Zealand)
NO Boreal Peatland LIFE/Parks and Wildlife Finland and Kone Foundation
NO Mexican National Council on Science and Technology
NO The Carl Tryggers Foundation (Stockholm, Sweden)
NO Natural Sciences and Engineering Research Council of Canada
NO French National Research Agency via the “Investment for the Future” program IDEALG
NO Commonwealth Scientific and Industrial Research Organisation (CSIRO)
NO Great Barrier Reef Marine Park Authority, the Fisheries Research and Development Corporation, the Australian Fisheries Management Authority, and Queensland Department of Primary Industries (QDPI)
NO Harold L. Castle Foundation (Hawái)
NO Clackamas County Water Environment Services River Health Stewardship Program
DS Docta Complutense
RD 9 jun 2025