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

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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.

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Aydın, A., Uysal, Ş. and Sarıer, Y. (2010). Analysing the Results of Pisa Maths Literacy in Terms of Social Justice and Equality in Educational Opportunities. Procedia Social and Behavioral Sciences, 2(2), 3537–3544.

Beblavy, M., Thum, A.-E., Potjagailo, G. & Werder, M. V. (2014). A Closer Look at The Effects of Within-School Ability Grouping in Secondary Schools: How Are Different Performers Affected?. IAAE 2014 Annual Conference, Queen Mary, University of London.

Carvalho, M., Gamboa, L. F. & Waltenberg F. D. (2013). Equality of Educational Opportunity Employing PISA Data: Taking Both Achievement and Access Into Account, Paper Prepared for the IARIW-IBGE Conference on Income, Wealth and Well-Being in Latin America, Rio de Janeiro, Brazil.

Coleman, J. S. (1966). Equality of Educational Opportunity, U.S. Government Printing Office, Washington.

Fertig, M. (2003a). Who's to Blame? The Determinants of German Students' Achievement in the PISA 2000 Study. IZA Discussion Paper No. 739, RWI Essen and IZA Bonn.

Fertig, M. (2003b). Educational Production, Endogenous Peer Group Formation and Class Composition — Evidence from the PISA 2000 Study. IZA DP No. 714.

Ferreira F. H. G. & Gignoux J. (2011). The Measurement of Educational Inequality: Achievement and Opportunity. PSE Working Papers n2011-38. 2011.

Giambona, F. & Porcu, M. (2015). Student Background Determinants of Reading Achievement in Italy. A Quantile Regression Analysis. International Journal of Educational Development, 44, 95–107.

Hao, L. & Naiman, Q. D. (2013). Quantile Regression. Sage Publications, Inc, California.

Heyneman, S. P. and Loxley, W. A. (1983). The Effect of Primary School Quality on Academic Achievement Across Twenty-Nine High and Low İncome Countries. American Journal of Sociology, 88 (6), 1162-1194.

Jalali, N. & Babanezhad, M. (2011). Quantile Regression due to Skewness and Outliers. Applied Mathematical Sciences, 5(39), 1947 – 1951.

Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33–50.

Koenker, R. & Hallock, K. F. (2001). Quantile Regression. Journal of Economic Perspectives, 15(4), 143–156.

Kurtoğlu, F. (2011). Quantıle Regresyon: Teorisi ve Uygulamaları. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü İstatistik Ana Bilim Dalı Ekonometri, Basılmamış Yükseklisans Tezi, Adana.

Lounkaew, K. (2013). Explaining Urban–Rural Differences in Educational Achievement in Thailand: Evidence From PISA Literacy Data. Economics of Education Review, 37, 213–225.

Martins, L. & Veiga, P. (2010). Do Inequalities in Parents’ Education Play An Important Role in PISA Students’ Mathematics Achievement Test Score Disparities?. Economics of Education Review, 29, 1016–1033.

Mosteller, F. & Tukey, J. (1977). Data Analysis and Regression: A Second Course in Statistics. Reading, Mass., Addison-Wesley.

Natkhov, T. & Kozina, N. (2012). Inequality of Educational Opportunity in a Cross-Section of Countries: Empirical Analysis of 2009 Pisa Data. National Research University Higher School of Economics, Moscow, Russia. Working paper Series: Education WP BRP 07/EDU/2012.

Oral, I. & Mcgivney, E. J. (2014). Türkiye Eğitim Sisteminde Eşitlik ve Akademik Başarı Araştırma Raporu ve Analiz. Eğitim Reformu Girişimi Raporları.

Rangvid, B.S. (2003). Evaluating Private School Quality in Denmark. Working Paper No. 03-2, Aarhus School of Business, Denmark.

Santos, M. E. (2007). Quality of Education in Argentina: Determinants and Distribution Using PISA 2000 Test Scores. Wellbeing and Social Policy 3(1), 93-119.

Schneeweis, N. & Winter-Ebmer, R. (2005). Peer Effects in Austrian Schools. Institute for Advanced Studies, Vienna.

Tansel, A. (2015). Inequality of Opportunities of Educational Achievement in Turkey over Time. IZA Discussion Paper No. 9005 April 2015

Yu, K., Lu, Z. & Stander, J. (2003). Quantile Regression: Applications and Current Research Areas. The Statistician 52(3), 333-334.

PISA 2009 Ulusal Ön Rapor, (2010). Millî Eğitim Bakanlığı, Eğitimi Araştırma ve Geliştirme Dairesi Başkanlığı, Ankara., erişim tarihi: 18.03.2016

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