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High order PLS path modeling to evaluate well-being merging traditional and big data: A longitudinal study

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High order PLS path modeling to evaluate well-being merging traditional and big data: A longitudinal study

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dc.contributor.author De Battisti, Francesca es_ES
dc.contributor.author Siletti, Elena es_ES
dc.date.accessioned 2020-09-08T11:55:25Z
dc.date.available 2020-09-08T11:55:25Z
dc.date.issued 2020-05-11
dc.identifier.isbn 9788490488324
dc.identifier.uri http://hdl.handle.net/10251/149602
dc.description.abstract [EN] We propose using high order partial least squares path modeling (PLS-PM) todefine a synthetic Italian well-being index merging traditional data,represented by the Quality of Life index proposed by “Il Sole 24 Ore”, andinformation provided by big data, represented by a Subjective Well-beingIndex (SWBI) performed extracting moods by Twitter. High order constructs,which allow to define a more abstract higher-level dimension and its moreconcrete lower-order sub-dimensions, have gained wide attention inapplications of PLS-PM, and many contributions in literature proposed theiruse to build composite indicators. The aim of the paper is to underline somecritical issues in the use of these models and to suggest the implementation ofa new spurious repeated indicator approach. Furthermore, following somerecommendations proposed on the use of PLS-PM in longitudinal studies, wecompare the situation in 2016 and 2017. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Web data es_ES
dc.subject Internet data es_ES
dc.subject Big data es_ES
dc.subject Qca es_ES
dc.subject Pls es_ES
dc.subject Sem es_ES
dc.subject Conference es_ES
dc.subject Well-being es_ES
dc.subject PLS-PM es_ES
dc.subject Hierarchical models es_ES
dc.title High order PLS path modeling to evaluate well-being merging traditional and big data: A longitudinal study es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2020.2020.11599
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation De Battisti, F.; Siletti, E. (2020). High order PLS path modeling to evaluate well-being merging traditional and big data: A longitudinal study. Editorial Universitat Politècnica de València. 95-102. https://doi.org/10.4995/CARMA2020.2020.11599 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Julio 08-09,2020 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2020/paper/view/11599 es_ES
dc.description.upvformatpinicio 95 es_ES
dc.description.upvformatpfin 102 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\11599 es_ES


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