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Student background determinants of reading achievement in Italy. A quantile regression analysis

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  • Giambona, Francesca
  • Porcu, Mariano

Abstract

In recent years determinants of students’ achievement has received much attention. Empirical studies have found that students’ characteristics, family background, school attended, and regional residence are major factors affecting student performance. In this paper, we analyze the 2009 OECD-PISA (spell PISA) survey to examine individual background characteristics influencing the reading achievement of Italian 15 years-old students using the quantile regression (QR) approach. The QR approach allows researchers to analyze changes in size and direction of predictor estimates on student performance across the entire distribution of reading achievement scores. Results indicate significant effects of predictors on reading achievement operating differently across quantiles, suggesting different pathways to achievement for low and high performing readers. In particular, some family background predictors (parental education, computer availability at home, and availability of a desk for homework at home), the school program attended and, the region of student residence play important but differing role for low and high performing readers. For example, parental education shows a positive effect on student reading, academic (general) programs perform better than vocational or technical, and Northern regions perform better than Center-Southern ones, with differentiated effects along the distribution of students’ reading scores. These findings should be carefully considered by policymakers when outlining strategies to enhance student performance at all levels along the reading continuum of low and high scores.

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  • Giambona, Francesca & Porcu, Mariano, 2015. "Student background determinants of reading achievement in Italy. A quantile regression analysis," International Journal of Educational Development, Elsevier, vol. 44(C), pages 95-107.
  • Handle: RePEc:eee:injoed:v:44:y:2015:i:c:p:95-107
    DOI: 10.1016/j.ijedudev.2015.07.005
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    1. Vargas-Montoya, Luis & Gimenez, Gregorio & Fernández-Gutiérrez, Marcos, 2023. "ICT use for learning and students' outcomes: Does the country's development level matter?," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    2. Gregorio Gimenez & Luis Vargas-Montoya, 2021. "ICT Use and Successful Learning: The Role of the Stock of Human Capital," Mathematics, MDPI, vol. 9(14), pages 1-15, July.
    3. Orazio Giancola & Luca Salmieri, 2020. "Family Background, School-Track and Macro-Area: the Complex Chains of Education Inequalities in Italy," Working Papers 4/20, Sapienza University of Rome, DISS.
    4. Cristian Barra & Marinella Boccia, 2022. "What matters in educational performance? Evidence from OECD and non-OECD countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4335-4394, December.
    5. Silvia Bianconcini & Stefania Mignani & Jacopo Mingozzi, 2023. "Assessing maths learning gaps using Italian longitudinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 911-930, September.
    6. Susana Faria & Maria Conceição Portela, 2016. "Student Performance in Mathematics using PISA-2009 data for Portugal," Working Papers de Gestão (Management Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.
    7. Camanho, Ana S. & Varriale, Luisa & Barbosa, Flávia & Sobral, Thiago, 2021. "Performance assessment of upper secondary schools in Italian regions using a circular pseudo-Malmquist index," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1188-1208.
    8. Tindara Addabbo & Maddalena Davoli & Marina Murat, 2018. "Is there an immigrant-gender gap in education? An empirical investigation based on PISA data from Italy," Department of Economics 0124, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    9. Romuald Foueka, 2020. "Analyse du différentiel de performances scolaires dans les pays PASEC sur la base de la régression quantile contrefactuelle," African Development Review, African Development Bank, vol. 32(4), pages 605-618, December.
    10. Barra, Cristian & Boccia, Marinella, 2019. "“The determinants of students' achievement: a difference between OECD and not OECD countries”," MPRA Paper 92561, University Library of Munich, Germany.
    11. Tindara Addabbo & Maddalena Davoli & Marina Murat, 2018. "Is there an immigrant-gender gap in education? An empirical investigation based on PISA data from Italy," Center for Economic Research (RECent) 136, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    12. Elias P. Kourkoutas & Stefanos G. Giakoumatos, 2023. "Statistical analysis and evaluation of Greek students’ background determinants on Science literacy," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 12(2), pages 1-2.
    13. Antonella D’Agostino & Francesco Schirripa Spagnolo & Nicola Salvati, 2022. "Studying the relationship between anxiety and school achievement: evidence from PISA data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 1-20, March.
    14. Sevda Gürsakal & Dilek Murat & Necmi Gürsakal, 2016. "Assessment of PISA 2012 Results With Quantile Regression Analysis Within The Context of Inequality In Educational Opportunity," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 41-54, September.

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