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The effect of socioeconomic status on academic achievement: A big data study across countries and time with integrative data analysis

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  • Mahmut Sami Yigiter

Abstract

The strong relationship between socioeconomic status (SES) and academic achievement has been the focus of much research over the last fifty years. However, there is a very limited number of studies that examine the SES of individual students and the SES of their peers in a disaggregated manner. This study examines the relationship between SES and academic achievement at both the student and school levels, using data from 54 countries from six cycles of the Trends in International Mathematics and Science Study (TIMSS) assessment over the past two decades. Integrative Data Analysis (IDA) is used to synthesize data across cycles and countries, and Hierarchical Linear Modeling (HLM) is used to account for the nested nature of students within schools. The results show that both individual and school SES have a significant positive effect on academic achievement, with school SES having a stronger effect. Moreover, most of the variance in the effects of individual and school SES is due to differences between countries. Income inequality reduces the effect of individual SES and leads to differences in SES and achievement between schools. On the other hand, an increase in individual SES increases the academic achievement of female students more, while male students attending schools with higher SES levels improve their academic achievement more. Increasing the individual or school SES of migrant students helps them reduce their academic achievement gaps. Moreover, the relationship between school SES and achievement was stronger in densely populated cities and large metropolitan areas. In line with the research findings, several recommendations are made to researchers and policy makers.

Suggested Citation

  • Mahmut Sami Yigiter, 2025. "The effect of socioeconomic status on academic achievement: A big data study across countries and time with integrative data analysis," PLOS ONE, Public Library of Science, vol. 20(10), pages 1-30, October.
  • Handle: RePEc:plo:pone00:0335485
    DOI: 10.1371/journal.pone.0335485
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