Predicting Higher Education Grades using Strategies Correcting for Panel Attrition - Giese Marco (De Gruyter, 2020)
This study aims to forecast the final grade of the first higher education degree which can be of considerable interest for higher education institutions to implement early warning systems, students themselves, or potential employers. The analysis is based on the National Education Panel Study (NEPS), a large German dataset covering many aspects of students' (educational) life. Since panel attrition concerns 35% of participants the Heckman correction and the inverse probability weight (IPW) estimator are used to reduce the estimation bias. A distinction is made between two scenarios, excluding dropout students and including them with a grade of 5.0. Some predictors reveal significant parameter estimates in the first but not in the second scenario, or vice versa, which means that dropout and study performance is not driven by the same variables. To get an early prediction of grades only variables of a pre-university episode were included in the first step. Afterward, variables of the early study phase are included. For the IPW estimator, the R2 improves from 0.202 to 0.593 (dropouts included) when adding the additional variables. The best predictors are the grades at secondary school, grades in the first exams, and the type of institution.