A1 Journal article (refereed)
Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences (2020)


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., . . . Sutherland, W. J. (2020). Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences. Nature Communications, 11, Article 6377. https://doi.org/10.1038/s41467-020-20142-y


JYU authors or editors


Publication details

All authors or editorsChristie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan C.; Amano, Tatsuya; Anton, Alvaro; Baldigo, Barry P.; Barrientos, Rafael; Bicknell, Jake E.; Buhl, Deborah A.; et al.

Journal or seriesNature Communications

eISSN2041-1723

Publication year2020

Volume11

Article number6377

PublisherNature Publishing Group

Publication countryUnited Kingdom

Publication languageEnglish

DOIhttps://doi.org/10.1038/s41467-020-20142-y

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/73343


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.


Keywordsenvironmental sciencessocial sciencesmethodologyresearch methodsreliability (general)randomised controlled trialspolitical decision makingevidence-based practices


Contributing organizations


Ministry reportingYes

Reporting Year2020

JUFO rating3


Last updated on 2024-26-03 at 09:19