Trait Preference Coefficients that scale economic weights

Respondent trait preferences (observed preference) were calculated using the 1000minds survey. The expected preference is calculated from the monetary worth of the trait improvement offered in the survey. The observed preferences were divided by the expected preferences to get trait preference coef...

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Detalles Bibliográficos
Autores principales: Adams, Chris, Byrne, Tim, Cole, Steven
Formato: Conjunto de datos
Lenguaje:Inglés
Publicado: WorldFish 2024
Materias:
Acceso en línea:https://hdl.handle.net/10568/163514
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author Adams, Chris
Byrne, Tim
Cole, Steven
author_browse Adams, Chris
Byrne, Tim
Cole, Steven
author_facet Adams, Chris
Byrne, Tim
Cole, Steven
author_sort Adams, Chris
collection Repository of Agricultural Research Outputs (CGSpace)
description Respondent trait preferences (observed preference) were calculated using the 1000minds survey. The expected preference is calculated from the monetary worth of the trait improvement offered in the survey. The observed preferences were divided by the expected preferences to get trait preference coefficients. The trait preference coefficients were then analysed using a principal component analysis followed by a bootstrapped (100,000) k-means (k = 4) clustering to determine trait preference clusters. The mean trait preference coefficient per cluster was then calculated
format Conjunto de datos
id CGSpace163514
institution CGIAR Consortium
language Inglés
publishDate 2024
publishDateRange 2024
publishDateSort 2024
publisher WorldFish
publisherStr WorldFish
record_format dspace
spelling CGSpace1635142025-12-12T20:15:51Z Trait Preference Coefficients that scale economic weights Adams, Chris Byrne, Tim Cole, Steven tilapia fish trait preference typology analysis loading factors food conversion ratio fish mortality Respondent trait preferences (observed preference) were calculated using the 1000minds survey. The expected preference is calculated from the monetary worth of the trait improvement offered in the survey. The observed preferences were divided by the expected preferences to get trait preference coefficients. The trait preference coefficients were then analysed using a principal component analysis followed by a bootstrapped (100,000) k-means (k = 4) clustering to determine trait preference clusters. The mean trait preference coefficient per cluster was then calculated 2024-11-20 2024-12-16T08:47:02Z 2024-12-16T08:47:02Z Dataset https://hdl.handle.net/10568/163514 en Limited Access WorldFish Chris Adams, Tim Byrne, Steven Cole. (20/11/2024). Trait Preference Coefficients that scale economic weights [Survey Data].
spellingShingle tilapia
fish
trait preference
typology analysis
loading factors
food conversion ratio
fish mortality
Adams, Chris
Byrne, Tim
Cole, Steven
Trait Preference Coefficients that scale economic weights
title Trait Preference Coefficients that scale economic weights
title_full Trait Preference Coefficients that scale economic weights
title_fullStr Trait Preference Coefficients that scale economic weights
title_full_unstemmed Trait Preference Coefficients that scale economic weights
title_short Trait Preference Coefficients that scale economic weights
title_sort trait preference coefficients that scale economic weights
topic tilapia
fish
trait preference
typology analysis
loading factors
food conversion ratio
fish mortality
url https://hdl.handle.net/10568/163514
work_keys_str_mv AT adamschris traitpreferencecoefficientsthatscaleeconomicweights
AT byrnetim traitpreferencecoefficientsthatscaleeconomicweights
AT colesteven traitpreferencecoefficientsthatscaleeconomicweights