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Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012

The present data article describes high-school drop-out related web activities in Canada, from 2004 to 2012, obtained mining Google Trends (GT), using high-school drop-out as key-word. The searches volumes were processed, correlated and cross-correlated with statistical data obtained at national and...

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Autores principales: Siri, Anna, Khabbache, Hicham, Al-Jafar, Ali, Martini, Mariano, Brigo, Francesco, Bragazzi, Nicola Luigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5071538/
https://www.ncbi.nlm.nih.gov/pubmed/27790632
http://dx.doi.org/10.1016/j.dib.2016.09.032
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author Siri, Anna
Khabbache, Hicham
Al-Jafar, Ali
Martini, Mariano
Brigo, Francesco
Bragazzi, Nicola Luigi
author_facet Siri, Anna
Khabbache, Hicham
Al-Jafar, Ali
Martini, Mariano
Brigo, Francesco
Bragazzi, Nicola Luigi
author_sort Siri, Anna
collection PubMed
description The present data article describes high-school drop-out related web activities in Canada, from 2004 to 2012, obtained mining Google Trends (GT), using high-school drop-out as key-word. The searches volumes were processed, correlated and cross-correlated with statistical data obtained at national and province level and broken down for gender. Further, an autoregressive moving-average (ARMA) model was used to model the GT-generated data. From a qualitative point of view, GT-generated relative search volumes (RSVs) reflect the decrease in drop-out rate. The peak in the Internet-related activities occurs in 2004 (56.35%, normalized value), and gradually declines to 40.59% (normalized value) in 2007. After, it remains substantially stable until 2012 (40.32%, normalized value). From a quantitative standpoint, the correlations between Canadian high-school drop-out rate and GT-generated RSVs in the study period (2004–2012) were statistically significant both using the drop-out rate for academic year and the 3-years moving average. Examining the data broken down by gender, the correlations were higher and statistically significant in males than in females. GT-based data for drop-out resulted best modeled by an ARMA(1,0) model. Considering the cross correlation of Canadian regions, all of them resulted statistically significant at lag 0, apart from for New Brunswick, Newfoundland and Labrador and the Prince Edward island. A number or cross-correlations resulted statistically significant also at lag −1 (namely, Alberta, Manitoba, New Brunswick and Saskatchewan).
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spelling pubmed-50715382016-10-27 Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012 Siri, Anna Khabbache, Hicham Al-Jafar, Ali Martini, Mariano Brigo, Francesco Bragazzi, Nicola Luigi Data Brief Data Article The present data article describes high-school drop-out related web activities in Canada, from 2004 to 2012, obtained mining Google Trends (GT), using high-school drop-out as key-word. The searches volumes were processed, correlated and cross-correlated with statistical data obtained at national and province level and broken down for gender. Further, an autoregressive moving-average (ARMA) model was used to model the GT-generated data. From a qualitative point of view, GT-generated relative search volumes (RSVs) reflect the decrease in drop-out rate. The peak in the Internet-related activities occurs in 2004 (56.35%, normalized value), and gradually declines to 40.59% (normalized value) in 2007. After, it remains substantially stable until 2012 (40.32%, normalized value). From a quantitative standpoint, the correlations between Canadian high-school drop-out rate and GT-generated RSVs in the study period (2004–2012) were statistically significant both using the drop-out rate for academic year and the 3-years moving average. Examining the data broken down by gender, the correlations were higher and statistically significant in males than in females. GT-based data for drop-out resulted best modeled by an ARMA(1,0) model. Considering the cross correlation of Canadian regions, all of them resulted statistically significant at lag 0, apart from for New Brunswick, Newfoundland and Labrador and the Prince Edward island. A number or cross-correlations resulted statistically significant also at lag −1 (namely, Alberta, Manitoba, New Brunswick and Saskatchewan). Elsevier 2016-10-13 /pmc/articles/PMC5071538/ /pubmed/27790632 http://dx.doi.org/10.1016/j.dib.2016.09.032 Text en © 2016 The Authors http://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 Data Article
Siri, Anna
Khabbache, Hicham
Al-Jafar, Ali
Martini, Mariano
Brigo, Francesco
Bragazzi, Nicola Luigi
Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
title Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
title_full Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
title_fullStr Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
title_full_unstemmed Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
title_short Infodemiological data of high-school drop-out related web searches in Canada correlating with real-world statistical data in the period 2004–2012
title_sort infodemiological data of high-school drop-out related web searches in canada correlating with real-world statistical data in the period 2004–2012
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5071538/
https://www.ncbi.nlm.nih.gov/pubmed/27790632
http://dx.doi.org/10.1016/j.dib.2016.09.032
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