Factors that affect Students’ performance in Science: An application using Gini-BMA methodology in PISA 2015 dataset

Anastasia Dimiski
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Anastasia Dimiski: University of Guelph

Review of Economic Analysis, 2021, vol. 13, issue 2, 157-211

Abstract: "Existing theoretical and empirical evidence on the determinants of students’ performance reveals a direct link between pre-primary education and achievement test scores in primary school. Relying on the first-of-its-kind 2015 wave data from the Programme of International Student Assessment (PISA), the present study analyses the associations between students’ performance in science and a broad set of variables, including regressors that proxy pre-primary education. Employing a Gini Regression Bayesian Model Averaging (BMA) approach to account for model uncertainty, it is found that non-attendance in pre-primary education is a robust determinant with a negative impact on students’ performance in science. This result is confirmed both under Gini-BMA and OLS-BMA methodology."

Keywords: "students’ performance; pre-primary education; Gini regression coefficient; BMA methodology; PISA" (search for similar items in EconPapers)
JEL-codes: C11 C38 I21 J24 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ren:journl:v:13:y:2021:i:2:p:157-211

DOI: 10.15353/rea.v13i2.1948

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