Forecasting Postgraduate Student Success: a Multivariate Regression Approach to Enhancing Academic and Professional Outcomes

This study will employ a mixed methods approach to investigate postgraduate engineering students' success within the Egyptian higher education context. The research examines six hypotheses related to success factors: the impact of academic background, psychological attributes, scholarly environment, research capabilities, external circumstances, and personal traits. Quantitatively, multivariate regression analysis examines the relationship between student success (measured through a composite score of publications, thesis quality, graduation time, and professional achievements) and corresponding predictive variables including CGPA, grit scales, laboratory access, critical thinking assessments, financial stability indicators, and leadership metrics. Qualitatively, in-depth interviews with faculty and students explore how these factors interact and influence academic outcomes. Data collection combines academic records, psychological assessments, and institutional surveys with thematic analysis of interview transcripts. The quantitative phase employs statistical measures to test the hypotheses, while the qualitative phase provides rich contextual insights into how these factors manifest within Egypt's unique educational landscape. This integrated approach aims to validate which factors most significantly predict postgraduate success, thereby informing evidence-based policies for student selection and support systems.