Investigation of the relationship between socioeconomic status and literacy in PISA Türkiye data

Yıl 2024, Cilt: 13 Sayı: 4, 360 - 378, 31.10.2024

Mehmet Can Demir , Kübra Atalay Kabasakal , Murat Doğan Şahin

https://doi.org/10.19128/turje.1474213

Öz

Previous researchers have identified socioeconomic status as a significant predictor of achievement/literacy. However, it is important to recognize that the influence of socioeconomic status on literacy may vary at different levels of socioeconomic status. Thus, this study analyzes the relationship between socioeconomic status and literacy scores for all domains in PISA Türkiye data from 2003 to 2022 through the Classification and Regression Trees and linear regression methods. Upon examining the results, separate investigations carried out for the lower and upper socioeconomic status groups indicate that R2 values were found to be equal to or greater than .80 in 37 out of the 42 analyses. From 2003 to 2009, the R2 values in both groups were considerably high; however, there has been a notable decline in subsequent periods. The year 2009 demonstrated particularly high R2 values by ESCS in all domains for both upper and lower groups. Consequently, socioeconomic status exhibited a greater predictive power on literacy scores across all domains in the lower socioeconomic group than upper socioeconomic group.

Anahtar Kelimeler

Classification and Regression Trees (CART), Economic social and cultural status (ESCS), Literacy Programme for International Student Assessment (PISA), Socioeconomic status

PISA Türkiye verilerinde sosyoekonomik düzey ve okuryazarlık arasındaki ilişkinin incelenmesi

Öz

Önceki araştırmalar, sosyoekonomik düzeyin başarının/okuryazarlığın önemli bir yordayıcısı olduğunu göstermiştir. Ancak, sosyoekonomik düzeyin okuryazarlık üzerindeki etkisinin farklı sosyoekonomik düzeylerde değişebileceğini kabul etmek önemlidir. Bu nedenle, bu çalışmada, 2003-2022 yılları arasındaki PISA Türkiye verilerinde tüm alanlar için sosyoekonomik düzey ve okuryazarlık puanları arasındaki ilişki Sınıflandırma ve Regresyon Ağaçları ve doğrusal regresyon yöntemleriyle analiz edilmiştir. Sonuçlar incelendiğinde, alt ve üst sosyoekonomik düzey için ayrı ayrı yapılan incelemeler, 42 analizin 37'sinde R2 değerlerinin .80'e eşit veya .80’den daha yüksek olduğunu göstermektedir. 2003'ten 2009'a kadar her iki grupta da R2 değerleri oldukça yüksektir; ancak sonraki yıllarda kayda değer bir düşüş yaşanmıştır. 2009 yılı hem üst hem de alt gruplar için tüm alanlarda ESCS'ye göre özellikle yüksek R2 değerleri göstermiştir. Sonuç olarak, sosyoekonomik düzey, alt sosyoekonomik grupta üst gruba göre daha iyi yordama gücüne sahiptir.

Anahtar Kelimeler

Ekonomik sosyal ve kültürel statü (ESCS), Okuryazarlık Sınıflama ve Regresyon Ağaçlar (CART), Sosyoekonomik düzey, Uluslararası Öğrenci Değerlendirme Programı

Kaynakça

• Akar, H. (2009). Challenges for schools in communities with internal migration flows: evidence from Turkey. International Journal of Educational Development, 30(3), 263–276. https://doi.org/10.1016/j.ijedudev.2009.11.003
• American Psychological Association. (2017, July 21). Education and socioeconomic status. https://www.apa.org/pi/ses/resources/publications/education
• American Psychological Association. (2019). Definition of socioeconomic status. https://www.apa.org/topics/socioeconomic-status/
• Arıkan, S., Özer, F., Şeker, V. & Ertaş, G. (2020). The importance of sample weights and plausible values in large-scale assessments. Journal of Measurement and Evaluation in Education and Psychology, 11(1), 43-60. https://doi.org/10.21031/epod.602765
• Avvisati, F. (2020). The measure of socio‑economic status in PISA: a review and some suggested improvements, Large-scale Assessments in Education, 8(8). https://doi.org/10.1186/s40536-020-00086-x
• Aydogdu, F. (2023). The Relationship between Compulsory Schooling Policy, Educational Equity and Student Achievement in Turkey and Portugal (Publication No. 30687189) [Doctoral dissertation, University of Toronto]. ProQuest Dissertations and Theses Global.
• Banerjee, P., & Eryilmaz, N. (2024). A critical evaluation of the validity of socioeconomic measures used in PISA. International Journal of Comparative Education and Development. https://doi.org/10.1108/IJCED-02-2023-0011
• Berliner, D. C. (2013). Effects of inequality and poverty vs. teachers and schooling on America's youth. Teachers College Record, 115(12), 1-26. https://doi.org/10.1177/016146811311501203
• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone C. J. (1984). Classification and Regression Trees. CRC Press.
• Bukodi, E., & Goldthorpe, J. H. (2013). Decomposing ‘social origins’: The effects of parents’ class, status, and education on the educational attainment of their children. European Sociological Review, 29(5), 1024–1039. https://doi.org/10.1093/esr/jcs079
• Caro, D. H., McDonald, J. T., & Willms, J. D. (2009). Socio-economic status and academic achievement trajectories from childhood to adolescence. Canadian Journal of Education, 32(3), 558-590.
• Chi, S., Liu, X., Wang, Z., & Won Han, S. (2018). Moderation of the effects of scientific inquiry activities on low SES students’ PISA 2015 science achievement by school teacher support and disciplinary climate in science classroom across gender. International Journal of Science Education, 40(11), 1284-1304. https://doi.org/10.1080/09500693.2018.1476742
• Chmielewski, A.K. (2019). The global increase in the socioeconomic achievement gap, 1964 to 2015. American Sociological Review, 84(3), 517-544. https://doi.org/10.1177/0003122419847165
• Coleman, J. S. (1968). Equality of educational opportunity. Integrated Education, 6(5), 19-28. https://doi.org/10.1080/0020486680060504
• Coşkun, B., & Karadağ, E. (2023). The effect of student and school characteristics on TIMSS 2015 science and mathematics achievement: The case of Türkiye. Journal of Pedagogical Research, 7(1), 203-227. https://doi.org/10.33902/JPR.202318875
• Çıngı, H., Kadılar, C., & Koçberber, G. (2009). A statistical approach for identifying the requirements of public primary and secondary schools based on districts in Turkey. Hacettepe University Journal of Education, 28(1), 105-116.
• De'ath, G. & Fabricius, K. (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology, 8(11), 3178-3192. https://doi.org/10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
• Duncan, G. J., & Murnane, R. J. (Eds.). (2011). Whither opportunity?: Rising inequality, schools, and children's life chances. Russell Sage Foundation.
• Erdem, C., & Kaya, M. (2021). Socioeconomic status and wellbeing as predictors of students’ academic achievement: evidence from a developing country. Journal of Psychologists and Counsellors in Schools, 33(2), 1-19. https://doi.org/10.1017/jgc.2021.10
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
• Gamazo, A., & Martínez-Abad, F. (2020). An exploration of factors linked to academic performance in PISA 2018 through data mining techniques. Frontiers in Psychology, 11 Article 575167. https://doi.org/10.3389/fpsyg.2020.575167
• Ganzeboom, H. B. G. & Treiman, D. J. (2003). Three internationally standardised measures for comparative research on occupational status. In J. H. P. Hoffmeyer-Zlotnik & C. Wolf (Eds.), Advances in cross-national comparison (pp. 159-193). https://doi.org/10.1007/978-1-4419-9186-7_9
• Gorard, S. (2006). The true impact of school diversity? In M. Hewlett, R. Pring, & M. Tulloch (Eds.), Comprehensive education: Evolution, achievement and new directions. University of Northampton Press.
• Hair, N. L., Hanson, J. L., Wolfe, B. L., & Pollak, S. D. (2015). Association of child poverty, brain development, and academic achievement. JAMA Pediatrics, 169(9), 822-829.
• Heyneman, S. P., & Loxley, W. A. (1983). The effect of primary-school quality on academic achievement across twenty-nine high-and low-income countries. American Journal of Sociology, 88(6), 1162-1194. https://doi.org/10.1086/227799
• International Labour Organization (2007). International Standard Classification of Occupations (ISCO) – 08. https://www.ilo.org/publications/international-standard-classification-occupations-isco-08
• Jehangir, K., Glas, C. A., & van den Berg, S. (2015). Exploring the relation between socio-economic status and reading achievement in PISA 2009 through an intercepts-and-slopes-as-outcomes paradigm. International Journal of Educational Research, 71, 1-15. https://doi.org/10.1016/j.ijer.2015.02.002
• Kim, S. W. (2019). Is socioeconomic status less predictive of achievement in east Asian countries? A systematic and meta-analytic review. International Journal of Educational Research, 97, 29–42. https://doi.org/10.1016/j.ijer.2019.05.009
• Lam, G. (2014). A theoretical framework of the relation between socioeconomic status and academic achievement of students. Education, 134(3), 326-331.
• Loh, W. Y. (2014). Fifty Years of Classification and Regression Trees. International Statistical Review, 82(3), 329–348. https://doi.org/10.1111/insr.12016
• Lee, J., & Borgonovi, F. (2022). Relationships between family socioeconomic status and mathematics achievement in OECD and non-OECD countries. Comparative Education Review, 66, 199–227. https://doi.org/10.1086/718930
• Ministry of National Education (2012). 12 Yıl Zorunlu Eğitim: Sorular-Cevaplar [12-years Compulsory Education: Questions and Answers]. Ankara. http://www.meb.gov.tr/duyurular/duyurular2012/12Yil_Soru_Cevaplar.pdf
• Neuman, M. (2022). PISA data clusters reveal student and school inequality that affects results. PLoS ONE 17(5): Article e0267040. https://doi.org/10.1371/journal.pone.0267040
• O'Connell, M. (2019). Is the impact of SES on educational performance overestimated? Evidence from the PISA survey. Intelligence, 75, 41-47. https://doi.org/10.1016/j.intell.2019.04.005
• OECD (2012). PISA 2009 Technical Report. https://www.oecd.org/pisa/pisaproducts/50036771.pdf
• OECD. (2018). Equity in Education: Breaking Down Barriers to Social Mobility. OECD Publishing. https://doi.org/10.1787/9789264073234-en
• OECD (2017). PISA 2015 Technical Report. https://www.oecd.org/pisa/data/2015-technical-report/PISA2015_TechRep_Final.pdf
• OECD (2019a). PISA 2018 Results (Volume III): What School Life Means for Students’ Lives. OECD Publishing. https://doi.org/10.1787/acd78851-en
• OECD (2019b). PISA 2018 Results (Volume I): What Students Know and Can Do. OECD Publishing. https://doi.org/10.1787/5f07c754-en
• OECD (2019c). PISA 2018 Results (Volume II): Where All Students Can Succeed. OECD Publishing. https://doi.org/10.1787/b5fd1b8f-en
• OECD (2020). PISA 2018 Technical Report. https://www.oecd.org/pisa/data/pisa2018technicalreport/
• OECD (2023). PISA 2022 Results (Volume II): Learning during – and from – disruption. https://doi.org/10.1787/a97db61c-en
• Orrù, G., Monaro, M., Conversano, C., Gemignani, A., & Sartori, G. (2020). Machine learning in psychometrics and psychological research. Frontiers in Psychology, 10, Article 2970. https://doi.org/10.3389/fpsyg.2019.02970
• Özdemir, C. (2016). Equity in the Turkish education system: A multilevel analysis of social background influences on the mathematics performance of 15-year-old students. European Educational Research Journal, 15(2), 193-217. https://doi.org/10.1177/1474904115627159
• Özer Özkan, Y. & Acar Güvendir, M. (2014). Socioeconomic factors of students’ relation to mathematic achievement: Comparison of PISA and ÖBBS. International Online Journal of Educational Sciences, 6(3), 776-789. http://dx.doi.org/10.15345/iojes.2014.03.020
• Perry, L. B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137-1162. https://doi.org/10.1177/016146811011200401
• Perry, L. B., Saatcioglu, A., & Mickelson, R. A. (2022). Does school SES matter less for high-performing students than for their lower-performing peers? A quantile regression analysis of PISA 2018 Australia. Large-scale Assessments in Education, 10(1), 1-29. https://doi.org/10.1186/s40536-022-00137-5
• R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
• Reardon, R. M. (2011). Elementary school principals' learning-centered leadership and educational outcomes: Implications for principals' professional development. Leadership and Policy in Schools, 10(1), 63-83. https://doi.org/10.1080/15700760903511798
• Schulz, W. (2005, April 7-11). Measuring the Socio-Economic Background of Students and Its Effect on Achievement on PISA 2000 and PISA 2003. Annual Meeting of the American Educational Research Association.
• Sirin, S. (2005). Socioeconomic status and academic achievement: a meta-review of research. Review of Educational Research, 75, 417–453. https://doi.org/10.3102/00346543075003417
• Strietholt, R., Gustafsson, J-E., Hogrebe, N., Rolfe, V., Rosén, M., Steinmann, I. & Yang Hansen, K. (2019). The impact of education policies on socioeconomic inequality in student achievement: a review of comparative studies. In L. Volante, S. Schnepf, J. Jerrim, & D. Klinger (Eds.) Socioeconomic inequality and student outcomes (pp 17-38). Springer. https://doi.org/10.1007/978-981-13-9863-6_2
• Tang P., Liu H. & Wen, H. (2021) Factors predicting collaborative problem solving: Based on the data from PISA 2015. Frontiers in Education 6, Article 619450. https://doi.org/10.3389/feduc.2021.619450
• Therneau, T., Atkinson, B., & Ripley, B. (2013). Rpart: Recursive Partitioning. R Package Version 4.1-3. http://CRAN.R-project.org/package=rpart
• Thomson, S. (2018). Achievement at school and socioeconomic background—an educational perspective. npj Science of Learning, 3(1). https://doi.org/10.1038/s41539-018-0022-0
• Von Stumm, S., & Plomin, R. (2014). Socioeconomic status and the growth of intelligence from infancy through adolescence. Intelligence, 48, 30–36. https://doi.org/10.1016/j.intell.2014.10.002
• Wang, F., King, R.B., & Leung, S.O. (2023). Why do East Asian students do so well in mathematics? A machine learning study. International Journal of Science and Mathematics Education, 21, 691–711. https://doi.org/10.1007/s10763-022-10262-w
• Williams, G. J. (2011). Data Mining with Rattle and R: The art of excavating data for knowledge discovery, series Use R! Springer.
• World Bank (2023). Promoting excellence in Turkey’s schools. (Report No. 77722). World Bank Group. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/944721468110943381/promoting-excellence-in-turkeys-schools
• Wu, M. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31(2-3), 114-128. https://doi.org/10.1016/j.stueduc.2005.05.005
• Yayan, B., & Berberoglu, G. (2004). A re-analysis of the TIMSS 1999 mathematics assessment data of the Turkish students. Studies in Educational Evaluation, 30(1), 87-104.