Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method (called S-SMART) and compare the statistical performance of it with that of the bootstrap through an application of them to the most advanced modelling technique, SEM, as an example. The evaluation of the statistical performances of S-SMART and the bootstrap with respect to the standard errors of the parameter estimates was conducted through a Monte Carlo simulation study. This work, while potentially benefiting educational and behavioural research, conceivably would also provide methodological support for other research areas, such as bioinformatics, biology, geosciences, astronomy, and ecology, where large samples are hard to obtain.