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

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