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Assessment of PISA 2012 Results With Quantile Regression Analysis Within The Context of Inequality In Educational Opportunity

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  • Sevda Gürsakal
  • Dilek Murat
  • Necmi Gürsakal

Abstract

The importance of educational opportunity inequality has been increasing within the context of education systems during recent years. In addition to quality in education, opportunity equality is among the significant paradigms in countries of high educational performance. Thus, it is of utmost importance to research the relationship between socio-economic characteristics of the students and achievement based on opportunity equality. Especially to remove the gap observed in Turkish literature is among the objectives of the present study. The main objective of the study is to assess the socio-demographic characteristics that affect the achievement of students in mathematics within the context of educational opportunity equality for PISA 2012 Turkey sample. Data analysis was conducted with quantile regression (QR) and classical linear regression (OLS). As a result, it was determined that students’ family background, familiarity with information and communication technology and school climate were affective on mathematics achievement. It was observed that as parentel education, educational resources at home, and index of familty wealth increased, mathematics achievement increased as well. It was also observed that time of computer use had a negative effect on achievement in mathematics. Furthermore, study findings identified that the achievement of male students was higher than females.

Suggested Citation

  • 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.
    • Handle: RePEc:anm:alpnmr:v:4:y:2016:i:2:p:41-54
    DOI: http://dx.doi.org/10.17093/aj.2016.4.2.5000186603
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    References listed on IDEAS

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    1. Márcia de Carvalho & Luis Fernando Gamboa & Fábio D. Waltenberg, 2012. "Equality of Educational Ppportunity Employing PISA Data: Taking both Achievement and Access into Account," Working Papers 277, ECINEQ, Society for the Study of Economic Inequality.
    2. Tansel, Aysit, 2015. "Inequality of Opportunities of Educational Achievement in Turkey over Time," IZA Discussion Papers 9005, Institute of Labor Economics (IZA).
    3. Francisco H. G. Ferreira & Jérémie Gignoux, 2014. "The Measurement of Educational Inequality: Achievement and Opportunity," The World Bank Economic Review, World Bank, vol. 28(2), pages 210-246.
    4. Nicole Schneeweis & Rudolf Winter-Ebmer, 2008. "Peer effects in Austrian schools," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 133-155, Springer.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Martins, Lurdes & Veiga, Paula, 2010. "Do inequalities in parents' education play an important role in PISA students' mathematics achievement test score disparities?," Economics of Education Review, Elsevier, vol. 29(6), pages 1016-1033, December.
    7. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence from the PISA 2000 Study," Royal Economic Society Annual Conference 2003 76, Royal Economic Society.
    8. Lounkaew, Kiatanantha, 2013. "Explaining urban–rural differences in educational achievement in Thailand: Evidence from PISA literacy data," Economics of Education Review, Elsevier, vol. 37(C), pages 213-225.
    9. 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.
    10. Timur Natkhov & Natalia Kozina, 2012. "Inequality of educational opportunity in a cross-section of countries. Empirical analysis of 2009 PISA data," HSE Working papers WP BRP 07/EDU/2012, National Research University Higher School of Economics.
    11. repec:hal:pseose:halshs-00646594 is not listed on IDEAS
    12. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
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    Cited by:

    1. Tommaso Agasisti & María Gil-Izquierdo & Seong Won Han, 2020. "ICT Use at home for school-related tasks: what is the effect on a student’s achievement? Empirical evidence from OECD PISA data," Education Economics, Taylor & Francis Journals, vol. 28(6), pages 601-620, November.
    2. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    3. Recep BİNDAK, 0. "Lojistik Regresyon Analizi ile Pisa Araştırmasında Öğrenci Başarısının Modellenmesi," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 14(28), pages 57-74.

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    More about this item

    Keywords

    Inequality of Educational Opportunity; Mathematics Score; PISA; Quantile Regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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