Multilevel Model Analysis to Investigate Predictor Variables in Mathematics Achievement PISA Data

Fani Yunida Anggraheni, Kismiantini Kismiantini, Fajar Ediyanto


This study aims to examine the relationship between predictor variables at the student and school levels and the interaction between variables in predicting mathematics achievement in Indonesia. Stratified analysis was implemented in Indonesia’s Programme for International Student Assessment (PISA) 2018 data. The variables of student level encompassed gender, economic, social, and cultural status (ESCS), metacognition, and learning time. This study revealed that the variables of ESCS, metacognition and learning time possessed a significant positive effect on mathematics achievement. The variables of school level are class size, school type, school size, and student-teacher ratio. This study demonstrated that only the data of class size produced a significant effect on mathematics achievement. Furthermore, the interaction between the learning time and class size also significantly affected learning achievement in mathematics. Therefore, variables increasing students’ mathematics achievement are ESCS, metacognition, learning time, class size, and interaction of learning time and class size.


Multilevel modelling;Mathematics achievement;PISA data;Student level;School level

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Areepattamannil, S. (2014). International Note: What factors are associated with reading, mathematics, and science literacy of Indian adolescents? A multilevel examination. Journal of Adolescence, 37(4), 367–372.

Avvisati, F., Echazarra, A., Givord, P., & Schwabe, M. (2018). What 15-year-old students in Indonesia know and can do. Programme for International Student Assessment (PISA) Result from PISA 2018, 1–10.

Bank, T. W. (2012). Learning outcomes in Thailand: what can we learn from international assessments? Report No 64801-TH.

Blatchford, P., Bassett, P., & Brown, P. (2011). Examining the effect of class size on classroom engagement and teacher-pupil interaction: Differences in relation to pupil prior attainment and primary vs. secondary schools. Learning and Instruction, 21(6), 715–730.

Chen, Q. (2016). A Multilevel Analysis of Singaporean Students’ Mathematics performance in PISA 2012 (pp. 17–33).

Eriksson, K., Helenius, O., & Ryve, A. (2019). Using TIMSS items to evaluate the effectiveness of different instructional practices. Instructional Science, 47(1), 1–18.

Giambona, F., & Porcu, M. (2018). School size and students’ achievement. Empirical evidences from PISA survey data. Socio-

Economic Planning Sciences, 64(March 2017), 66–77.

Gromping, U. (2015). Multilevel modeling using R. Journal of Statistical Software, 62 (Book Review 1).

Hox, J. J., Moerbeek, M., & Schoot, R. Van De. (2018). Multilevel Analysis Techniques and Applications. In Library of Congress Cataloging-in-Publication Data. Library of Congress Cataloging-in-Publication Data.

Johnson, R. B., & Christensen, L. (2014). Educational research quantitative, qualitative, and mixed approaches. Library of Congress Cataloging-in-Publication Data.

Karakolidis, A., Pitsia, V., & Emvalotis, A. (2016). Examining students’ achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece. International Journal of Educational Research, 79, 106–115.

Ketonen, E. E., & Hotulainen, R. (2019). Development of low-stakes mathematics and literacy test scores during lower secondary school – A multilevel pattern-centered analysis of student and classroom differences. Contemporary Educational Psychology, 59(July), 101793.

Milford, T., Ross, S. P., & Anderson, J. O. (2010). An opportunity to better understand schooling: The growing presence of PISA in the AMERICAS*. International Journal of Science and Mathematics Education, 8(3), 453–473.

Muszyński, M. (2015). Learning strategies and reading. February 2016.

O’Grady, G., Yew, E. H. J., Goh, K. P. L., & Schmidt, H. G. (2012). One-Day, One-Problem. Springer Singapore Heidelberg.

OECD. (2017a). Main Survey School Sampling Preparation Manual Overview. Pisa 2018, February.

OECD. (2017b). PISA 2015 Technical Report. OECD Publishing, 1–468.

OECD. (2017c). PISA for Development Assessment and Analytical Framework. OECD Publishing.

OECD. (2019a). How PISA results are reported: What is a PISA score? Programme for International Student Assessment (PISA) Result from PISA 2018, I (Volume I), 41–47.

OECD. (2019b). PISA 2018 insights and interpretations. OECD Publishing, 64. 2018 Insights and Interpretations FINAL PDF.pdf

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.

Peugh, J. L. (2010). A practical guide to multilevel modeling. Journal of School Psychology, 48(1), 85–112.

Pinheiro, J., Bates, D., DebRoy, S., & Sarkar, D. (2012). nlme: Fit and Compare Gaussian Linear and Nonlinear Mixed-Effects Models. R. Package Version 3.1-104.

Rabash, J., Steele, F., Browne, W. J., & Goldstein, H. (2015). A user’s guide to MLwiN. In England: Centered for Multilevel Modelling. England: Centered for Multilevel Modelling.

Sakellariou, C. (2017). Private or public-school advantage? Evidence from 40 countries using PISA 2012-Mathematics. 1, 582.

Stacey, K. (2011). The PISA view of mathematical literacy in Indonesia. Journal on Mathematics Education, 2(2), 95–126.

Tarling, R. (2009). Statistical Modelling for Social Researchers. In Statistical Modelling for Social Researchers. Statistical Modelling for Social Researchers.

Teodorović, J. (2012). Student background factors influencing student achievement in Serbia. Educational Studies, 38(1), 89–110.

Thien, L. M., Darmawan, I. G. N., & Ong, M. Y. (2015). Affective characteristics and mathematics performance in Indonesia, Malaysia, and Thailand: what can PISA 2012 data tell us? Large-Scale Assessments in Education, 3(1).

Van de Walle, J. A., Karp, K. S., Bay-Williams, J. M., Wray, J., & Brown, E. T. (2019). Elementary and Middle School Mathematics: Teaching Developmentally (10th Ed.). NY, NY: Pearson.



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Southeast Asian Mathematics Education Journal
SEAMEO Regional Centre for QITEP in Mathematics
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