In educational effectiveness research, multilevel data analyses are often used because research units (most frequently, pupils or teachers) are studied that are nested in groups (schools and classes). This hierarchical data structure complicates designing the study because the structure has to be taken into account when approximating the accuracy of estimation and the power for statistical testing, which should be sufficient to reach meaningful conclusions. Accuracy and power, both referred to as efficiency, can be optimized by carefully choosing the number of units to sample at each of the levels, taking into account the available resources and costs of sampling at these levels. We complement the findings that are found in the literature with regard to designing multilevel studies and propose a simulation approach that can be used to help making study-specific decisions. (Contains 1 note, 2 tables and 5 figures.)