How to enhance web survey data using metered, geolocation, visual and voice data?

After briefly summarizing why there is a need to enhance web survey data, this paper explains how metered, geolocation, visual and voice data could help to supplement conventional web survey data, particularly when mobile participation is high. It presents expected benefits of these four data types...

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Bibliographic Details
Author: Revilla, Melanie
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/60438
Online Access:http://hdl.handle.net/10230/60438
http://dx.doi.org/10.18148/srm/2022.v16i1.8013
Access Level:Open access
Keyword:Geolocation
Measurement
Metered data
(Mobile) Web surveys
Visual data
Voice recording
Data quality
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oai_identifier_str oai:recercat.cat:10230/60438
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network_name_str España
repository_id_str
spelling How to enhance web survey data using metered, geolocation, visual and voice data?Revilla, MelanieGeolocationMeasurementMetered data(Mobile) Web surveysVisual dataVoice recordingData qualityAfter briefly summarizing why there is a need to enhance web survey data, this paper explains how metered, geolocation, visual and voice data could help to supplement conventional web survey data, particularly when mobile participation is high. It presents expected benefits of these four data types in terms of respondents’ burden, data quality and possible new insights, as well as a number of expected disadvantages, both on the respondents’ and researchers’ sides. Finally, the paper discusses what is still missing and the next steps to turn these newopportunities into realities.European Survey Research Association (ESRA)202420242022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/60438http://dx.doi.org/10.18148/srm/2022.v16i1.8013reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésSurvey Research Methods. 2022;16(1):1-12.© 2022 Author(s) CC BY-NC 4.0.https://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessoai:recercat.cat:10230/604382026-05-29T05:05:01Z
dc.title.none.fl_str_mv How to enhance web survey data using metered, geolocation, visual and voice data?
title How to enhance web survey data using metered, geolocation, visual and voice data?
spellingShingle How to enhance web survey data using metered, geolocation, visual and voice data?
Revilla, Melanie
Geolocation
Measurement
Metered data
(Mobile) Web surveys
Visual data
Voice recording
Data quality
title_short How to enhance web survey data using metered, geolocation, visual and voice data?
title_full How to enhance web survey data using metered, geolocation, visual and voice data?
title_fullStr How to enhance web survey data using metered, geolocation, visual and voice data?
title_full_unstemmed How to enhance web survey data using metered, geolocation, visual and voice data?
title_sort How to enhance web survey data using metered, geolocation, visual and voice data?
dc.creator.none.fl_str_mv Revilla, Melanie
author Revilla, Melanie
author_facet Revilla, Melanie
author_role author
dc.subject.none.fl_str_mv Geolocation
Measurement
Metered data
(Mobile) Web surveys
Visual data
Voice recording
Data quality
topic Geolocation
Measurement
Metered data
(Mobile) Web surveys
Visual data
Voice recording
Data quality
description After briefly summarizing why there is a need to enhance web survey data, this paper explains how metered, geolocation, visual and voice data could help to supplement conventional web survey data, particularly when mobile participation is high. It presents expected benefits of these four data types in terms of respondents’ burden, data quality and possible new insights, as well as a number of expected disadvantages, both on the respondents’ and researchers’ sides. Finally, the paper discusses what is still missing and the next steps to turn these newopportunities into realities.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/60438
http://dx.doi.org/10.18148/srm/2022.v16i1.8013
url http://hdl.handle.net/10230/60438
http://dx.doi.org/10.18148/srm/2022.v16i1.8013
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Survey Research Methods. 2022;16(1):1-12.
dc.rights.none.fl_str_mv © 2022 Author(s) CC BY-NC 4.0.
https://creativecommons.org/licenses/by-nc/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2022 Author(s) CC BY-NC 4.0.
https://creativecommons.org/licenses/by-nc/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv European Survey Research Association (ESRA)
publisher.none.fl_str_mv European Survey Research Association (ESRA)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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