A Bayesian non-parametric modeling to estimate student response to ICT investment

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dc.contributor.author Cabras, Stefano
dc.contributor.author Tena Horrillo, Juan de Dios
dc.date.accessioned 2021-07-07T11:03:29Z
dc.date.available 2021-07-07T11:03:29Z
dc.date.issued 2016
dc.identifier.bibliographicCitation Cabras, S. & Tena Horrillo, J. D. (2016). A Bayesian non-parametric modeling to estimate student response to ICT investment. Journal of Applied Stadistics, 43(14), pp. 2627-2642.
dc.identifier.issn 0266-4763
dc.identifier.uri http://hdl.handle.net/10016/33015
dc.description.abstract This paper estimates the causal impact of investment in information and communication technologies (ICT) on student performances in mathematics as measured in the Program for International Student Assessment (PISA) 2012 for Spain. To do this we apply a new methodology in this context known as Bayesian Additive Regression Trees that has important advantages over more standard parametric specifications. Results indicate that ICT has a moderate positive effect on math scores. In addition, we analyze how this effect interacts with variables related to school features and student socioeconomic status, finding that ICT investment is especially beneficial for students from a low socioeconomic background.
dc.description.sponsorship Stefano Cabras has been supported by Ministerio de Ciencia e Innovación grant MTM2013-42323, ECO2012-38442, RYC-2012-11455, by Ministero dell'Istruzione, dell'Univesità e della Ricerca of Italy and by Regi one Autonoma della Sardegn a CRP-59903. Juan de Dios Tena Horrillo has been supported by Ministerio de Educación y Ciencia, ECO2009-08100 and ECO2012-32401.
dc.format.extent 16
dc.language.iso eng
dc.publisher Taylor & Francis
dc.relation.hasversion http://hdl.handle.net/10016/19916
dc.rights © 2016 Taylor & Francis
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Regression trees
dc.subject.other Causality
dc.subject.other ICT
dc.subject.other Bayesian statistics
dc.subject.other BART
dc.title A Bayesian non-parametric modeling to estimate student response to ICT investment
dc.type article
dc.subject.eciencia Estadística
dc.subject.eciencia Informática
dc.subject.eciencia Matemáticas
dc.identifier.doi https://doi.org/10.1080/02664763.2016.1142946
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2009-08100
dc.relation.projectID Gobierno de España. ECO2012-38442
dc.relation.projectID Gobierno de España. ECO2012-32401
dc.relation.projectID Gobierno de España. RYC-2012-11455
dc.relation.projectID Gobierno de España. MTM2013-42323
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 2627
dc.identifier.publicationissue 14
dc.identifier.publicationlastpage 2642
dc.identifier.publicationtitle Journal of Applied Stadistics
dc.identifier.publicationvolume 43
dc.identifier.uxxi AR/0000018204
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
dc.affiliation.dpto UC3M. Departamento de Estadística
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