Hierarchical data sets arise when the data for lower units (e.g., individuals such as students, clients, and citizens) are nested within higher units (e.g., groups such as classes, hospitals, and regions). In data collection for experimental research, estimating the required sample size beforehand is a fundamental question for obtaining sufficient statistical power and precision of the focused parameters. The present research extends previous research from Heo and Leon (2008) and Usami (2011b), by deriving closed-form formulas for determining the required sample size to test effects in experimental research with hierarchical data, and by focusing on both multisite-randomized trials (MRTs) and cluster-randomized trials (CRTs). These formulas consider both statistical power and the width of the confidence interval of a standardized effect size, on the basis of estimates from a random-intercept model for three-level data that considers both balanced and unbalanced designs. These formulas also address some important results, such as the lower bounds of the needed units at the highest levels.