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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. 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.
    2. 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.
    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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