Generalization from single-case designs can be achieved by replicating individual studies across different experimental units and settings. When replications are available, their findings can be summarized using effect size measurements and integrated through meta-analyses. Several procedures are available for quantifying the magnitude of treatment effect in N = 1 designs, and some of them are studied in this article. Monte Carlo simulations were used to generate different data patterns (trend, level change, and slope change). The experimental conditions simulated were defined by the degrees of serial dependence and phase length. Out of all the effect size indices studied, the percentage of nonoverlapping data and standardized mean difference proved to be less affected by autocorrelation and to perform better for shorter data series. The regression-based procedures proposed specifically for single-case designs did not differentiate between data patterns as well as did simpler indices.