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Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
AU - Christie, A.P.
AU - Abecasis, D.
AU - Adjeroud, M.
AU - Alonso, J.C.
AU - Amano, T.
AU - Anton, A.
AU - Baldigo, B.P.
AU - Barrientos, R.
AU - Bicknell, J.E.
AU - Buhl, D.A.
AU - Cebrian, J.
AU - Ceia, R.S.
AU - Cibils-Martina, L.
AU - Clarke, S.
AU - Claudet, J.
AU - Craig, M.D.
AU - Davoult, D.
AU - De Backer, A.
AU - Donovan, M.K.
AU - Eddy, T.D.
AU - França, F.M.
AU - Gardner, J.P.A.
AU - Harris, B.P.
AU - Huusko, A.
AU - Jones, I.L.
AU - Kelaher, B.P.
AU - Kotiaho, J.S.
AU - López-Baucells, A.
AU - Major, H.L.
AU - Mäki-Petäys, A.
AU - Martín, B.
AU - Martín, C.A.
AU - Martin, P.A.
AU - Mateos-Molina, D.
AU - McConnaughey, R.A.
AU - Meroni, M.
AU - Meyer, C.F.J.
AU - Mills, K.
AU - Montefalcone, M.
AU - Noreika, N.
AU - Palacín, C.
AU - Pande, A.
AU - Pitcher, C.R.
AU - Ponce, C.
AU - Rinella, M.
AU - Rocha, R.
AU - Ruiz-Delgado, M.C.
AU - Schmitter-Soto, J.J.
AU - Shaffer, J.A.
AU - Sharma, S.
AU - Sher, A.A.
AU - Stagnol, D.
AU - Stanley, T.R.
AU - Stokesbury, K.D.E.
AU - Torres, A.
AU - Tully, O.
AU - Vehanen, T.
AU - Watts, C.
AU - Zhao, Q.
AU - Sutherland, W.J.
PY - 2020/12/11
Y1 - 2020/12/11
N2 - 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.
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.
KW - biodiversity
KW - data set
KW - decision making
KW - numerical model
KW - article
KW - conservation biology
KW - human
KW - intervention study
KW - prevalence
KW - randomized controlled trial (topic)
KW - sociology
KW - synthesis
U2 - 10.1038/s41467-020-20142-y
DO - 10.1038/s41467-020-20142-y
M3 - Journal article
VL - 11
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 6377
ER -