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
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/142663
Ver los metadatos del registro completo
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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 Técnicas2025-10-22T11:01:21Zoai: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:34982025-10-22 11:01:21.646CONICET 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 |
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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 |
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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 |
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Springer |
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Springer |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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