The effect of the dropout and student satisfaction on student success: an instrumental variable quantile regression approach

Purpose The purpose of this study is to investigate the impact of dismissal policies on student dropout rates and subsequent graduation outcomes in higher education. By utilizing Instrumental Variable Quantile Regression (IVQR), the research aims to provide a robust analysis of how variations in dismissal policies influence student success across different quantiles. The study seeks to highlight the significance of student satisfaction and retention in predicting graduation rates, ultimately informing educational policy and interventions designed to enhance student outcomes and improve the overall quality of academic programs. Design/methodology/approach The study employs Instrumental Variable Quantile Regression (IVQR) to estimate the causal effects of student dropout and satisfaction on graduation outcomes. It addresses endogeneity issues by using the dismissal policy as an instrument for dropout rates. The analysis includes various quantile functions to explore the heterogeneous impact of these variables across different levels of student success. The study compares IVQR results with standard Quantile Regression (QR) and Ordinary Least Squares (OLS) estimates to assess the influence of endogeneity on the conclusions drawn regarding student outcomes and institutional policies. Findings The study finds that dismissal policies significantly influence student dropout rates and graduation outcomes, with varying effects across different quantiles. Higher dropout rates are associated with stricter dismissal policies, while increased student satisfaction correlates with improved graduation rates. The analysis reveals that the impact of dropout on graduation rates is particularly pronounced at lower quantiles, indicating that students facing academic challenges are more affected by dismissal policies. Additionally, factors such as study time and program characteristics play a critical role in enhancing student success, underscoring the need for effective institutional policies to support student retention and satisfaction. Research limitations/implications The study acknowledges several limitations, including its focus on data from the Netherlands, which may limit the generalizability of findings to other educational contexts. Additionally, while the use of Instrumental Variable Quantile Regression helps address endogeneity, potential unobserved variables influencing student satisfaction and dropout rates may still exist. The reliance on administrative data may also overlook qualitative aspects of student experiences. Future research should explore longitudinal data and incorporate diverse educational settings to validate findings and enhance understanding of the mechanisms linking dismissal policies, student satisfaction, and graduation outcomes. Originality/value This study contributes original insights by employing Instrumental Variable Quantile Regression to analyze the nuanced effects of dismissal policies on student dropout and graduation rates. It highlights the importance of considering variations across quantiles, revealing that the impact of dropout is not uniform but varies significantly among students facing different academic challenges. The findings underscore the critical role of student satisfaction and program characteristics in enhancing educational outcomes. By providing evidence-based recommendations for policy interventions, this research adds value to the discourse on improving student retention and success in higher education, particularly in the context of evolving educational landscapes.