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

Christie, Alec P. and Abecasis, David and Adjeroud, Mehdi and Alonso, Juan C. and Amano, Tatsuya and Anton, Alvaro and Baldigo, Barry P. and Barrientos, Rafael and Bicknell, Jake E. and Buhl, Deborah A. and Cebrian, Just and Ceia, Ricardo S. and Cibils-Martina, Luciana and Clarke, Sarah and Claudet, Joachim and Craig, Michael D. and Davoult, Dominique and De Backer, Annelies and Donovan, Mary K. and Eddy, Tyler D. and Franca, Filipe M. and Gardner, Jonathan P. A. and Harris, Bradley P. and Huusko, Ari and Jones, Ian L. and Kelaher, Brendan P. and Kotiaho, Janne S. and Lopez-Baucells, Adria and Major, Heather L. and Maki-Petays, Aki and Martin, Beatriz and Mateos-Molina, Daniel and McConnaughey, Robert A. and Meroni, Michele and Meyer, Christoph F. J. and Mills, Kade and Montefalcone, Monica and Noreika, Norbertas and Palacin, Carlos and Pande, Anjali and Pitcher, C. Roland and Ponce, Carlos and Rinella, Matt and Rocha, Ricardo and Ruiz-Delgado, Maria C. and Schmitter-Soto, Juan J. and Shaffer, Jill A. and Sharma, Shailesh and Sher, Anna A. and Stagnol, Doriane and Stanley, Thomas R. and Stokesbury, Kevin D. E. and Torres, Aurora and Tully, Oliver and Vehanen, Teppo and Watts, Corinne and Zhao, Qingyuan and Sutherland, William J. (2020). Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nature Communications. 11 , 6377
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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. Randomised controlled experiments are the gold standard for scientific inference, but environmental and social scientists often rely on different study designs. Here the authors analyse the use of six common study designs in the fields of biodiversity conservation and social intervention, and quantify the biases in their estimates.

Authors/Creators:Christie, Alec P. and Abecasis, David and Adjeroud, Mehdi and Alonso, Juan C. and Amano, Tatsuya and Anton, Alvaro and Baldigo, Barry P. and Barrientos, Rafael and Bicknell, Jake E. and Buhl, Deborah A. and Cebrian, Just and Ceia, Ricardo S. and Cibils-Martina, Luciana and Clarke, Sarah and Claudet, Joachim and Craig, Michael D. and Davoult, Dominique and De Backer, Annelies and Donovan, Mary K. and Eddy, Tyler D. and Franca, Filipe M. and Gardner, Jonathan P. A. and Harris, Bradley P. and Huusko, Ari and Jones, Ian L. and Kelaher, Brendan P. and Kotiaho, Janne S. and Lopez-Baucells, Adria and Major, Heather L. and Maki-Petays, Aki and Martin, Beatriz and Mateos-Molina, Daniel and McConnaughey, Robert A. and Meroni, Michele and Meyer, Christoph F. J. and Mills, Kade and Montefalcone, Monica and Noreika, Norbertas and Palacin, Carlos and Pande, Anjali and Pitcher, C. Roland and Ponce, Carlos and Rinella, Matt and Rocha, Ricardo and Ruiz-Delgado, Maria C. and Schmitter-Soto, Juan J. and Shaffer, Jill A. and Sharma, Shailesh and Sher, Anna A. and Stagnol, Doriane and Stanley, Thomas R. and Stokesbury, Kevin D. E. and Torres, Aurora and Tully, Oliver and Vehanen, Teppo and Watts, Corinne and Zhao, Qingyuan and Sutherland, William J.
Title:Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
Series Name/Journal:Nature Communications
Year of publishing :2020
Volume:11
Article number:6377
Number of Pages:11
Publisher:NATURE RESEARCH
ISSN:2041-1723
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 107 Other Natural Sciences > Other Natural Sciences not elsewhere specified
(A) Swedish standard research categories 2011 > 5 Social Sciences > 509 Other Social Sciences > Social Sciences Interdisciplinary (Peace and Conflict Research and Studies on Sustainable Society)
URN:NBN:urn:nbn:se:slu:epsilon-p-110093
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-110093
Additional ID:
Type of IDID
DOI10.1038/s41467-020-20142-y
Web of Science (WoS)000600150800017
ID Code:21444
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Ecology
(S) > Dept. of Ecology
Deposited By: SLUpub Connector
Deposited On:21 Jan 2021 13:44
Metadata Last Modified:21 Jan 2021 13:51

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