This study conducts a comparative analysis of Survey-weighted Multinomial Logit (SW-MNLR) and Probit (MNP) models to evaluate their effectiveness in capturing the complexities of apprenticeship dynamics in the Ghanaian context. Our core aim is twofold: to assess the comparative performance of these two prominent statistical models and identify the key factors influencing young Ghanaians to choose apprenticeship in the informal sector. Utilizing simulation-based techniques, we explore how these models account for key determinants such as parental and teacher influence, financial constraints, educational attainment, and limitations to apprenticeship options. Our findings indicate that while both models offer valuable insights into these determinants, the SW-MNLR model provides a more nuanced understanding of the data, particularly in handling sampling biases and class imbalances. While the SW-MNLR is more accurate and precise in prediction, the MNP is rather more sensitive to the data. This research fills a significant gap in the literature by offering a robust methodological framework for analyzing apprenticeship decisions, with implications for policymakers aiming to optimize educational and vocational training programs. Leveraging SW-MNLR for Policy Analysis, its superior predictive accuracy suggests it's a more robust and reliable tool for analyzing apprenticeship decisions in Ghana. Policymakers should prioritize using survey-weighted logistic regression approaches when conducting research to inform apprenticeship decisions or workforce development policies, as it will likely yield more accurate predictions and a clearer understanding of potential outcomes. The study underscores the importance of selecting appropriate statistical tools to inform policy implementation and development, especially apprenticeship decisions in the informal sector, ultimately contributing to effective educational strategies and identifying key factors. © 2025
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