Do Extracurricular Activities give ‘Extra’ on Academic Performance? Evidence from Propensity Score Matching Methods

Abstract

This study compares different statistical methods to determine whether participating
in extracurricular activities helps improve students’ academic performance. Utilizing a dataset
of 1,000 students, the study balances students who did and did not take part in extracurriculars
by adjusting for factors like study hours and attendance. It compares Nearest Mahalanobis
Distance, 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 reveal
that IPW performs best in the covariates balance, reducing nearly all standardized mean
differences to near zero while retaining the majority of the dataset. Nearest Neighbor Matching
with Caliper and Optimal Pair Matching also perform well with significant treatment effect
estimates and relatively strong model fits. However, each method involves trade-offs in which
IPW excels in covariate balance but has a higher AIC, a slight compromise in model fit, while
Nearest Neighbor Matching with Caliper offers a balance between precision, model fit, and
sample retention. In contrast, CEM provides strong covariate balance for categorical variables
but results in significant sample loss, demonstrating the trade-off between strict matching criteria
and practical applicability. Conversely, Nearest Neighbor Matching without Caliper performed
poorly in balancing covariates. As evidenced by the average treatment effect estimates derived
from the propensity score matching (PSM) methods, this study concludes that participation in
extracurricular activities has a positive and significant impact on students' academic
performance, with study hours, attendance, and resource accessibility emerging as critical factors
as well. The novelty of this study is in comparing multiple statistical matching approaches side
by side in an educational context, providing guidance for researchers and policymakers.