Do Extracurricular Activities give ‘Extra’ on Academic Performance? Evidence from Propensity Score Matching Methods - Nozaleda, Bryan (Politeknik Statistika STIS, 2025)
This study compares different statistical methods to determine whether participatingin extracurricular activities helps improve students’ academic performance. Utilizing a datasetof 1,000 students, the study balances students who did and did not take part in extracurricularsby adjusting for factors like study hours and attendance. It compares Nearest MahalanobisDistance, Nearest Neighbor Matching (with and without a caliper), Optimal Pair Matching,Optimal Full Matching, Coarsened Exact Matching (CEM), and Inverse Probability Weighting(IPW) based on covariate balance, sample retention, and average treatment effect. Results revealthat IPW performs best in the covariates balance, reducing nearly all standardized meandifferences to near zero while retaining the majority of the dataset. Nearest Neighbor Matchingwith Caliper and Optimal Pair Matching also perform well with significant treatment effectestimates and relatively strong model fits. However, each method involves trade-offs in whichIPW excels in covariate balance but has a higher AIC, a slight compromise in model fit, whileNearest Neighbor Matching with Caliper offers a balance between precision, model fit, andsample retention. In contrast, CEM provides strong covariate balance for categorical variablesbut results in significant sample loss, demonstrating the trade-off between strict matching criteriaand practical applicability. Conversely, Nearest Neighbor Matching without Caliper performedpoorly in balancing covariates. As evidenced by the average treatment effect estimates derivedfrom the propensity score matching (PSM) methods, this study concludes that participation inextracurricular activities has a positive and significant impact on students' academicperformance, with study hours, attendance, and resource accessibility emerging as critical factorsas well. The novelty of this study is in comparing multiple statistical matching approaches sideby side in an educational context, providing guidance for researchers and policymakers.