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by Anastasia Dimiski
| Institution: | University of Guelph |
|---|---|
| Department: | Department of Economics and Finance |
| Degree: | PhD |
| Year: | 2022 |
| Keywords: | Students’ performance; pre-primary education; Gini regression coefficient; BMA methodology; PISA |
| Posted: | 3/25/2025 |
| Record ID: | 2255724 |
| Full text PDF: | https://atrium.lib.uoguelph.ca/xmlui/handle/10214/26778 |
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.
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