Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

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

Christie, A.P., Abecasis, D., Adjeroud, M., Alonso, J.C., Amano, T., Anton, A., Baldigo, B.P., Barrientos, R., Bicknell, J.E., Buhl, D.A., Cebrian, J., Ceia, R.S., Cibils-Martina, L., Clarke, S., Claudet, J., Craig, M.D., Davoult, D., De Backer, A., Donovan, M.K., Eddy, T.D., França, F.M., Gardner, J.P.A., Harris, B.P., Huusko, A., Jones, I.L., Kelaher, B.P., Kotiaho, J.S., López-Baucells, A., Major, H.L., Mäki-Petäys, A., Martin, B., Martín, C.A., Martin, P.A., Mateos-Molina, D., McConnaughey, R.A., Meroni, M., Meyer, C.F.J., Mills, K., Montefalcone, M., Noreika, N., Palacín, C., Pande, A., Pitcher, C.R., Ponce, C., Rinella, M., Rocha, R., Ruiz-Delgado, M.C., Schmitter-Soto, J.J., Shaffer, J.A., Sharma, S., Sher, A.A., Stagnol, D., Stanley, T.R., Stokesbury, K.D.E., Torres, A., Tully, O., Vehanen, T., Watts, C., Zhao, Q. and Sutherland, W.J. (2020) Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nature Communications, 11 (1). Art. 6377.

PDF - Published Version
Download (7MB) | Preview
Free to read:
*No subscription required


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: Journal Article
Murdoch Affiliation(s): Environmental and Conservation Sciences
Publisher: Springer Nature
Copyright: © 2020 The Authors.
Item Control Page Item Control Page


Downloads per month over past year