In the present study, we proposed a modification in one of the most frequently applied effect-size procedures in single-case data analysis: the percentage of nonoverlapping data. In contrast with other techniques, the calculus and interpretation of this procedure are straightforward and can be easily complemented by visual inspection of the graphed data. Although the percentage of nonoverlapping data has been found to perform reasonably well in N = 1 data, the magnitude of effect estimates that it yields can be distorted by trend and autocorrelation. Therefore, the data-correction procedure focuses on removing the baseline trend from data prior to estimating the change produced in the behavior as a result of intervention. A simulation study was carried out in order to compare the original and the modified procedures in several experimental conditions. The results suggest that the new proposal is unaffected by trend and autocorrelation and that it can be used in case of unstable baselines and sequentially related measurements.