6533b824fe1ef96bd1280aaa
RESEARCH PRODUCT
Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
Ari HuuskoWilliam J. SutherlandTyler D. EddyTyler D. EddyTyler D. EddyDoriane StagnolDeborah A. BuhlMatt RinellaTatsuya AmanoTeppo VehanenThomas R. StanleyC. Roland PitcherDavid AbecasisRicardo S. CeiaAnjali PandeAnjali PandeBrendan P. KelaherShailesh SharmaJuan C. AlonsoAdrià López-baucellsAdrià López-baucellsAdrià López-baucellsLuciana Cibils-martinaLuciana Cibils-martinaHeather L. MajorJill A. ShafferMonica MontefalconeBradley P. HarrisJake E. BicknellAnnelies De BackerIan L. JonesAki Mäki-petäysJuan J. Schmitter-sotoMaría C. Ruiz-delgadoCorinne WattsOliver TullyNorbertas NoreikaNorbertas NoreikaKade MillsChristoph F. J. MeyerChristoph F. J. MeyerChristoph F. J. MeyerJust CebrianMichele MeroniQingyuan ZhaoRafael BarrientosDominique DavoultMichael D. CraigMichael D. CraigCarlos PonceMary K. DonovanMary K. DonovanAlec P. ChristieJonathan P. A. GardnerCarlos PalacínAlvaro AntónRobert A. McconnaugheyBeatriz MartínAurora TorresAurora TorresDaniel Mateos-molinaFilipe FrançaSarah ClarkeKevin D. E. StokesburyJanne S. KotiahoMehdi AdjeroudRicardo RochaRicardo RochaRicardo RochaAnna A. SherBarry P. BaldigoCarlos A. MartínPhilip A. MartinJoachim Claudetsubject
0106 biological sciencesResearch designScientific communitySCIENTIFIC COMMUNITYMedio ambiente naturalsosiaalitieteetPsychological interventionGeneral Physics and AstronomySocial SciencesQH7501 natural sciencesEnvironmental impact//purl.org/becyt/ford/1 [https]010104 statistics & probability/706/648CredibilityPrevalenceSocial scienceComputingMilieux_MISCELLANEOUSGEMultidisciplinaryEcologyQarticleSampling (statistics)Biodiversitynäyttöön perustuvat käytännötsatunnaistetut vertailukokeetENVIRONMENTAL IMPACTResearch designResearch DesignScale (social sciences)[SDE]Environmental SciencesH1ScienceEnvironment010603 evolutionary biologyGeneral Biochemistry Genetics and Molecular BiologySocial sciencesBiastutkimusmenetelmätQH541/704/172/4081Humans0101 mathematics//purl.org/becyt/ford/1.6 [https]ympäristötieteetpoliittinen päätöksentekoClinical study designmetodologia/706/689General Chemistry15. Life on landEcologíaLiteraturePairwise comparisonObservational study/631/158luotettavuusBias; Biodiversity; Ecology; Environment; Humans; Literature; Prevalence; Research Design; Social Sciencesdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-11 | Nature Communications |