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

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.