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

Cabras, Stefano; Tena Horrillo, Juan de Dios

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

Author(s): Cabras, Stefano; Tena Horrillo, Juan de Dios

Publisher: Taylor & Francis

Issued date: 2016

Citation: 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.

ISSN: 0266-4763

DOI: https://doi.org/10.1080/02664763.2016.1142946

xmlui.dri2xhtml.METS-1.0.item-contributor-funder: Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia e Innovación (España)

Sponsor: 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.

Other version: http://hdl.handle.net/10016/19916

Project: Gobierno de España. ECO2009-08100
Gobierno de España. ECO2012-38442
Gobierno de España. ECO2012-32401
Gobierno de España. RYC-2012-11455
Gobierno de España. MTM2013-42323

Keywords: Regression trees , Causality , ICT , Bayesian statistics , BART

URI: http://hdl.handle.net/10016/33015

Rights: © 2016 Taylor & Francis
Atribución-NoComercial-SinDerivadas 3.0 España

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 met [+]

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