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

Autores
Christie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan Carlos; Amano, Tatsuya; Anton, Alvaro; Baldigo, Barry P.; Barrientos, Rafael; Bicknell, Jake E.; Buhl, Deborah A.; Cebrian, Just; Ceia, Ricardo S.; Cibils Martina, Luciana; Clarke, Sarah; Claudet, Joachim; Craig, Michael D.; Davoult, Dominique; De Backer, Annelies; Donovan, Mary K.; Eddy, Tyler D.; França, Filipe M.; Gardner, Jonathan P. A.; Harris, Bradley P.; Huusko, Ari; Jones, Ian L.; Kelaher, Brendan P.; Kotiaho, Janne S.; López Baucells, Adrià; Major, Heather L.; Mäki Petäys, Aki
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Christie, Alec P.. University of Cambridge; Reino Unido
Fil: Abecasis, David. Universidad de Algarve. Centro de Ciencias del Mar; Portugal
Fil: Adjeroud, Mehdi. Université de Perpignan; Francia. Institut de Recherche Pour Le Developpement; Francia
Fil: Alonso, Juan Carlos. Consejo Superior de Investigaciones Científicas. Museo Nacional de Ciencias Naturales; España
Fil: Amano, Tatsuya. University of Queensland; Australia
Fil: Anton, Alvaro. Universidad del País Vasco. Facultad de Educación de Bilbao; España
Fil: Baldigo, Barry P.. United States Geological Survey; Estados Unidos
Fil: Barrientos, Rafael. Universidad Complutense de Madrid; España
Fil: Bicknell, Jake E.. University of Kent; Reino Unido
Fil: Buhl, Deborah A.. United States Geological Survey; Estados Unidos
Fil: Cebrian, Just. Mississippi State University; Estados Unidos
Fil: Ceia, Ricardo S.. Universidad de Coimbra; Portugal
Fil: Cibils Martina, Luciana. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Clarke, Sarah. Marine Institute; Irlanda
Fil: Claudet, Joachim. Universite de Paris; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Craig, Michael D.. University of Western Australia; Australia. Murdoch University; Australia
Fil: Davoult, Dominique. Sorbonne University; Francia
Fil: De Backer, Annelies. Flanders Research Institute for Agriculture, Fisheries and Food; Bélgica
Fil: Donovan, Mary K.. University of California; Estados Unidos. University of Hawaii at Manoa; Estados Unidos
Fil: Eddy, Tyler D.. University of South Carolina; Estados Unidos. Memorial University of Newfoundland; Canadá. Victoria University of Wellington; Nueva Zelanda
Fil: França, Filipe M.. Lancaster University; Reino Unido
Fil: Gardner, Jonathan P. A.. Victoria University of Wellington; Nueva Zelanda
Fil: Harris, Bradley P.. Alaska Pacific University; Estados Unidos
Fil: Huusko, Ari. Natural Resources Institute Finland; Finlandia
Fil: Jones, Ian L.. Memorial University of Newfoundland; Canadá
Fil: Kelaher, Brendan P.. Southern Cross University; Australia
Fil: Kotiaho, Janne S.. Universidad de Jyvaskyla; Finlandia
Fil: López Baucells, Adrià. Universidad de Lisboa; Portugal. Smithsonian Tropical Research Institute; Panamá. Universidad Nacional de Colombia. Instituto de Investigaciones Amazonicas; Colombia. Museo de Ciencias Naturales de Granollers; España
Fil: Major, Heather L.. University of New Brunswick; Canadá
Fil: Mäki Petäys, Aki. Voimalohi Oy; Finlandia. University of Oulu; Finlandia
Materia
ECOLOGY
ENVIRONMENTAL IMPACT
SCIENTIFIC COMMUNITY
SOCIAL SCIENCES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/142663

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spelling Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciencesChristie, Alec P.Abecasis, DavidAdjeroud, MehdiAlonso, Juan CarlosAmano, TatsuyaAnton, AlvaroBaldigo, Barry P.Barrientos, RafaelBicknell, Jake E.Buhl, Deborah A.Cebrian, JustCeia, Ricardo S.Cibils Martina, LucianaClarke, SarahClaudet, JoachimCraig, Michael D.Davoult, DominiqueDe Backer, AnneliesDonovan, Mary K.Eddy, Tyler D.França, Filipe M.Gardner, Jonathan P. A.Harris, Bradley P.Huusko, AriJones, Ian L.Kelaher, Brendan P.Kotiaho, Janne S.López Baucells, AdriàMajor, Heather L.Mäki Petäys, AkiECOLOGYENVIRONMENTAL IMPACTSCIENTIFIC COMMUNITYSOCIAL SCIENCEShttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Building 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.Fil: Christie, Alec P.. University of Cambridge; Reino UnidoFil: Abecasis, David. Universidad de Algarve. Centro de Ciencias del Mar; PortugalFil: Adjeroud, Mehdi. Université de Perpignan; Francia. Institut de Recherche Pour Le Developpement; FranciaFil: Alonso, Juan Carlos. Consejo Superior de Investigaciones Científicas. Museo Nacional de Ciencias Naturales; EspañaFil: Amano, Tatsuya. University of Queensland; AustraliaFil: Anton, Alvaro. Universidad del País Vasco. Facultad de Educación de Bilbao; EspañaFil: Baldigo, Barry P.. United States Geological Survey; Estados UnidosFil: Barrientos, Rafael. Universidad Complutense de Madrid; EspañaFil: Bicknell, Jake E.. University of Kent; Reino UnidoFil: Buhl, Deborah A.. United States Geological Survey; Estados UnidosFil: Cebrian, Just. Mississippi State University; Estados UnidosFil: Ceia, Ricardo S.. Universidad de Coimbra; PortugalFil: Cibils Martina, Luciana. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Clarke, Sarah. Marine Institute; IrlandaFil: Claudet, Joachim. Universite de Paris; Francia. Centre National de la Recherche Scientifique; FranciaFil: Craig, Michael D.. University of Western Australia; Australia. Murdoch University; AustraliaFil: Davoult, Dominique. Sorbonne University; FranciaFil: De Backer, Annelies. Flanders Research Institute for Agriculture, Fisheries and Food; BélgicaFil: Donovan, Mary K.. University of California; Estados Unidos. University of Hawaii at Manoa; Estados UnidosFil: Eddy, Tyler D.. University of South Carolina; Estados Unidos. Memorial University of Newfoundland; Canadá. Victoria University of Wellington; Nueva ZelandaFil: França, Filipe M.. Lancaster University; Reino UnidoFil: Gardner, Jonathan P. A.. Victoria University of Wellington; Nueva ZelandaFil: Harris, Bradley P.. Alaska Pacific University; Estados UnidosFil: Huusko, Ari. Natural Resources Institute Finland; FinlandiaFil: Jones, Ian L.. Memorial University of Newfoundland; CanadáFil: Kelaher, Brendan P.. Southern Cross University; AustraliaFil: Kotiaho, Janne S.. Universidad de Jyvaskyla; FinlandiaFil: López Baucells, Adrià. Universidad de Lisboa; Portugal. Smithsonian Tropical Research Institute; Panamá. Universidad Nacional de Colombia. Instituto de Investigaciones Amazonicas; Colombia. Museo de Ciencias Naturales de Granollers; EspañaFil: Major, Heather L.. University of New Brunswick; CanadáFil: Mäki Petäys, Aki. Voimalohi Oy; Finlandia. University of Oulu; FinlandiaSpringer2020-12-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/142663Christie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan Carlos; Amano, Tatsuya; et al.; Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences; Springer; Nature Communications; 11; 1; 11-12-2020; 1-112041-1723CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41467-020-20142-yinfo:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-020-20142-yinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:20Zoai:ri.conicet.gov.ar:11336/142663instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:33:21.161CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
title Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
spellingShingle Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
Christie, Alec P.
ECOLOGY
ENVIRONMENTAL IMPACT
SCIENTIFIC COMMUNITY
SOCIAL SCIENCES
title_short Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
title_full Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
title_fullStr Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
title_full_unstemmed Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
title_sort Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
dc.creator.none.fl_str_mv Christie, Alec P.
Abecasis, David
Adjeroud, Mehdi
Alonso, Juan Carlos
Amano, Tatsuya
Anton, Alvaro
Baldigo, Barry P.
Barrientos, Rafael
Bicknell, Jake E.
Buhl, Deborah A.
Cebrian, Just
Ceia, Ricardo S.
Cibils Martina, Luciana
Clarke, Sarah
Claudet, Joachim
Craig, Michael D.
Davoult, Dominique
De Backer, Annelies
Donovan, Mary K.
Eddy, Tyler D.
França, Filipe M.
Gardner, Jonathan P. A.
Harris, Bradley P.
Huusko, Ari
Jones, Ian L.
Kelaher, Brendan P.
Kotiaho, Janne S.
López Baucells, Adrià
Major, Heather L.
Mäki Petäys, Aki
author Christie, Alec P.
author_facet Christie, Alec P.
Abecasis, David
Adjeroud, Mehdi
Alonso, Juan Carlos
Amano, Tatsuya
Anton, Alvaro
Baldigo, Barry P.
Barrientos, Rafael
Bicknell, Jake E.
Buhl, Deborah A.
Cebrian, Just
Ceia, Ricardo S.
Cibils Martina, Luciana
Clarke, Sarah
Claudet, Joachim
Craig, Michael D.
Davoult, Dominique
De Backer, Annelies
Donovan, Mary K.
Eddy, Tyler D.
França, Filipe M.
Gardner, Jonathan P. A.
Harris, Bradley P.
Huusko, Ari
Jones, Ian L.
Kelaher, Brendan P.
Kotiaho, Janne S.
López Baucells, Adrià
Major, Heather L.
Mäki Petäys, Aki
author_role author
author2 Abecasis, David
Adjeroud, Mehdi
Alonso, Juan Carlos
Amano, Tatsuya
Anton, Alvaro
Baldigo, Barry P.
Barrientos, Rafael
Bicknell, Jake E.
Buhl, Deborah A.
Cebrian, Just
Ceia, Ricardo S.
Cibils Martina, Luciana
Clarke, Sarah
Claudet, Joachim
Craig, Michael D.
Davoult, Dominique
De Backer, Annelies
Donovan, Mary K.
Eddy, Tyler D.
França, Filipe M.
Gardner, Jonathan P. A.
Harris, Bradley P.
Huusko, Ari
Jones, Ian L.
Kelaher, Brendan P.
Kotiaho, Janne S.
López Baucells, Adrià
Major, Heather L.
Mäki Petäys, Aki
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ECOLOGY
ENVIRONMENTAL IMPACT
SCIENTIFIC COMMUNITY
SOCIAL SCIENCES
topic ECOLOGY
ENVIRONMENTAL IMPACT
SCIENTIFIC COMMUNITY
SOCIAL SCIENCES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Christie, Alec P.. University of Cambridge; Reino Unido
Fil: Abecasis, David. Universidad de Algarve. Centro de Ciencias del Mar; Portugal
Fil: Adjeroud, Mehdi. Université de Perpignan; Francia. Institut de Recherche Pour Le Developpement; Francia
Fil: Alonso, Juan Carlos. Consejo Superior de Investigaciones Científicas. Museo Nacional de Ciencias Naturales; España
Fil: Amano, Tatsuya. University of Queensland; Australia
Fil: Anton, Alvaro. Universidad del País Vasco. Facultad de Educación de Bilbao; España
Fil: Baldigo, Barry P.. United States Geological Survey; Estados Unidos
Fil: Barrientos, Rafael. Universidad Complutense de Madrid; España
Fil: Bicknell, Jake E.. University of Kent; Reino Unido
Fil: Buhl, Deborah A.. United States Geological Survey; Estados Unidos
Fil: Cebrian, Just. Mississippi State University; Estados Unidos
Fil: Ceia, Ricardo S.. Universidad de Coimbra; Portugal
Fil: Cibils Martina, Luciana. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Ciencias Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Clarke, Sarah. Marine Institute; Irlanda
Fil: Claudet, Joachim. Universite de Paris; Francia. Centre National de la Recherche Scientifique; Francia
Fil: Craig, Michael D.. University of Western Australia; Australia. Murdoch University; Australia
Fil: Davoult, Dominique. Sorbonne University; Francia
Fil: De Backer, Annelies. Flanders Research Institute for Agriculture, Fisheries and Food; Bélgica
Fil: Donovan, Mary K.. University of California; Estados Unidos. University of Hawaii at Manoa; Estados Unidos
Fil: Eddy, Tyler D.. University of South Carolina; Estados Unidos. Memorial University of Newfoundland; Canadá. Victoria University of Wellington; Nueva Zelanda
Fil: França, Filipe M.. Lancaster University; Reino Unido
Fil: Gardner, Jonathan P. A.. Victoria University of Wellington; Nueva Zelanda
Fil: Harris, Bradley P.. Alaska Pacific University; Estados Unidos
Fil: Huusko, Ari. Natural Resources Institute Finland; Finlandia
Fil: Jones, Ian L.. Memorial University of Newfoundland; Canadá
Fil: Kelaher, Brendan P.. Southern Cross University; Australia
Fil: Kotiaho, Janne S.. Universidad de Jyvaskyla; Finlandia
Fil: López Baucells, Adrià. Universidad de Lisboa; Portugal. Smithsonian Tropical Research Institute; Panamá. Universidad Nacional de Colombia. Instituto de Investigaciones Amazonicas; Colombia. Museo de Ciencias Naturales de Granollers; España
Fil: Major, Heather L.. University of New Brunswick; Canadá
Fil: Mäki Petäys, Aki. Voimalohi Oy; Finlandia. University of Oulu; Finlandia
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/142663
Christie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan Carlos; Amano, Tatsuya; et al.; Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences; Springer; Nature Communications; 11; 1; 11-12-2020; 1-11
2041-1723
CONICET Digital
CONICET
url http://hdl.handle.net/11336/142663
identifier_str_mv Christie, Alec P.; Abecasis, David; Adjeroud, Mehdi; Alonso, Juan Carlos; Amano, Tatsuya; et al.; Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences; Springer; Nature Communications; 11; 1; 11-12-2020; 1-11
2041-1723
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41467-020-20142-y
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-020-20142-y
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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