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Using Trellis software to enhance high-quality large-scale network data collection in the field

Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their so...

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Detalles Bibliográficos
Autores principales: Lungeanu, Alina, McKnight, Mark, Negron, Rennie, Munar, Wolfgang, Christakis, Nicholas A., Contractor, Noshir S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117970/
https://www.ncbi.nlm.nih.gov/pubmed/34219904
http://dx.doi.org/10.1016/j.socnet.2021.02.007
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author Lungeanu, Alina
McKnight, Mark
Negron, Rennie
Munar, Wolfgang
Christakis, Nicholas A.
Contractor, Noshir S.
author_facet Lungeanu, Alina
McKnight, Mark
Negron, Rennie
Munar, Wolfgang
Christakis, Nicholas A.
Contractor, Noshir S.
author_sort Lungeanu, Alina
collection PubMed
description Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis’ ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location.
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spelling pubmed-81179702021-07-01 Using Trellis software to enhance high-quality large-scale network data collection in the field Lungeanu, Alina McKnight, Mark Negron, Rennie Munar, Wolfgang Christakis, Nicholas A. Contractor, Noshir S. Soc Networks Article Trellis is a mobile platform created by the Human Nature Lab at the Yale Institute for Network Science to collect high-quality, location-aware, off-line/online, multi-lingual, multi-relational social network and behavior data in hard-to-reach communities. Respondents use Trellis to identify their social contacts by name and photograph, a procedure especially useful in low-literacy populations or in contexts where names may be similar or confusing. We use social network data collected from 1,969 adult respondents in two villages in Kenya to demonstrate Trellis’ ability to provide unprecedented metadata to monitor and report on the data collection process including artifactual variability based on surveyors, time of day, or location. Elsevier 2021-07 /pmc/articles/PMC8117970/ /pubmed/34219904 http://dx.doi.org/10.1016/j.socnet.2021.02.007 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lungeanu, Alina
McKnight, Mark
Negron, Rennie
Munar, Wolfgang
Christakis, Nicholas A.
Contractor, Noshir S.
Using Trellis software to enhance high-quality large-scale network data collection in the field
title Using Trellis software to enhance high-quality large-scale network data collection in the field
title_full Using Trellis software to enhance high-quality large-scale network data collection in the field
title_fullStr Using Trellis software to enhance high-quality large-scale network data collection in the field
title_full_unstemmed Using Trellis software to enhance high-quality large-scale network data collection in the field
title_short Using Trellis software to enhance high-quality large-scale network data collection in the field
title_sort using trellis software to enhance high-quality large-scale network data collection in the field
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117970/
https://www.ncbi.nlm.nih.gov/pubmed/34219904
http://dx.doi.org/10.1016/j.socnet.2021.02.007
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